diff --git a/rl_coach/traces/Atari_A3C_LSTM_breakout/trace.csv b/rl_coach/traces/Atari_A3C_LSTM_breakout/trace.csv
index b1d1e92..0556673 100644
--- a/rl_coach/traces/Atari_A3C_LSTM_breakout/trace.csv
+++ b/rl_coach/traces/Atari_A3C_LSTM_breakout/trace.csv
@@ -1,196 +1,201 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
-1,0.0,1.0,24.0,1.0,24.0,24.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,0.0,1.0,24.0,1.0,24.0,48.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,0.0,1.0,52.0,1.0,52.0,100.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-5,0.0,1.0,24.0,1.0,24.0,148.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,0.0,1.0,24.0,1.0,24.0,172.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,0.0,1.0,24.0,1.0,24.0,196.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,0.0,1.0,24.0,1.0,24.0,220.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,0.0,1.0,24.0,1.0,24.0,244.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,0.0,1.0,24.0,1.0,24.0,268.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,0.0,1.0,24.0,1.0,24.0,292.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+9,0.0,1.0,24.0,1.0,24.0,244.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,
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+11,0.0,1.0,24.0,1.0,24.0,292.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,
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+21,0.0,1.0,24.0,1.0,24.0,558.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,
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+25,0.0,1.0,24.0,1.0,24.0,624.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,
+26,0.0,1.0,24.0,1.0,24.0,648.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,
+27,0.0,1.0,24.0,1.0,24.0,672.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Atari_A3C_LSTM_pong/trace.csv b/rl_coach/traces/Atari_A3C_LSTM_pong/trace.csv
index b0d6eb7..c7ebbe4 100644
--- a/rl_coach/traces/Atari_A3C_LSTM_pong/trace.csv
+++ b/rl_coach/traces/Atari_A3C_LSTM_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_A3C_LSTM_space_invaders/trace.csv b/rl_coach/traces/Atari_A3C_LSTM_space_invaders/trace.csv
index a274660..3c3359a 100644
--- a/rl_coach/traces/Atari_A3C_LSTM_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_A3C_LSTM_space_invaders/trace.csv
@@ -1,26 +1,26 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
-1,0.0,1.0,248.0,1.0,248.0,248.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,0.0,1.0,123.0,1.0,123.0,371.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,0.0,1.0,88.0,1.0,88.0,459.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,0.0,1.0,187.0,1.0,187.0,646.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,0.0,1.0,86.0,1.0,86.0,732.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,0.0,1.0,331.0,1.0,331.0,1063.0,0.0,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,37.0,0.0,753.0,1.0,753.0,1816.0,0.0,18.0,275.0,0.0,,,,,,,,,,,,0.19276376,0.24904153,0.8257747,0.00013455142,1.7914727,0.00030057304,1.7917566000000003,1.7904335,0.2119369695934143,0.4029896256601249,1.8961015939712524,-0.038109242916107185,0.059407155999999996,0.06025871,0.20562454,-0.0059952493999999995,0.10879397,0.13543734,0.42884704,1.0928144400000001e-07,0.37946862,0.4819447,1.3135536,-0.039071497000000004
-8,43.0,0.0,107.0,1.0,107.0,1923.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,0.015239561,0.0024996148,0.019516254,0.011843238999999998,1.7906417000000001,0.0002779353,1.7917081999999998,1.790199,-0.027609226256608964,0.013555497071717059,-0.002493098378181457,-0.054812923073768616,0.23526458,0.013103152,0.25880286,0.2149402,0.0010658596000000002,4.0590476e-05,0.0011093322,0.0010099161,-0.04934317,0.011472803,-0.039747406,-0.06998761
-9,47.0,0.0,73.0,1.0,73.0,1996.0,0.0,1.0,25.0,0.0,,,,,,,,,,,,0.36924744,0.48345366,1.0529541999999998,0.027382427999999997,1.7904778000000001,0.00029410556,1.7917029999999998,1.7902383999999998,0.2182698796192805,0.4074832854303497,0.9812830686569214,-0.05661928653717041,0.29025623,0.01401644,0.3092764,0.27106556,0.12317172,0.1714658,0.36566097,0.0019109361999999999,0.39213285,0.6270856,1.2789414,-0.0569774
-10,60.0,0.0,251.0,1.0,251.0,2247.0,0.0,4.0,30.0,0.0,,,,,,,,,,,,0.43863640000000004,0.85959023,2.5217259999999997,0.035312783,1.7856493000000002,0.0015894786999999999,1.7916044999999998,1.7811941999999998,0.11047004585464797,0.4511292058995869,1.8077605962753296,-0.14487385749816895,0.61056226,0.07753438,0.79911625,0.4961661,0.1471402,0.31318584,0.8826119,0.0050534373,0.1947745,0.6780483,1.7730703,-0.1461431
-11,66.0,0.0,121.0,1.0,121.0,2368.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,0.14871785,0.02547766,0.19108213,0.12211705,1.7780668000000002,0.0027863213,1.7913043000000002,1.7759546000000002,-0.10607111632823944,0.05572927975960492,-0.009624600410461426,-0.19753050804138186,1.0220673,0.034096994,1.0672632,0.95422196,0.021255326,0.0012313497,0.023416747999999998,0.019953651,-0.18968049,0.016377756,-0.16716708,-0.21171494
-12,71.0,0.0,99.0,1.0,99.0,2467.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,0.15555012,0.04610389,0.23511228,0.12260077,1.7795043000000001,0.0023333197,1.7912293999999997,1.7773554,-0.09951297342777253,0.055938959522401716,-0.003077983856201172,-0.1911371946334839,1.0086769,0.036691166000000004,1.0868968,0.96416116,0.02108366,0.0023025707,0.02351839,0.018744798,-0.17643596,0.02231863,-0.14677188,-0.20941082
-13,98.0,0.0,534.0,1.0,534.0,3001.0,0.0,13.0,340.0,0.0,,,,,,,,,,,,0.7437658,1.1363528999999999,3.4128056000000004,0.025902914,1.7773186,0.003927723,1.7911369,1.7642651000000003,0.1266731635882304,0.41158095902457936,1.7730363607406616,-0.30260801315307617,0.9577929000000001,0.23765793,1.6504846999999998,0.7122954,0.15221074,0.20607093,0.757879,0.008616385,0.22623166,0.6171821,1.5555726,-0.30132666
-14,102.0,0.0,73.0,1.0,73.0,3074.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,0.21133159,0.027693717000000003,0.24578243,0.17797336,1.7719979,0.004124344,1.7904103,1.7675488999999998,-0.18784045577049252,0.09607193868630864,-0.01820123195648193,-0.3348844051361084,1.8157914000000002,0.008718997,1.8383793000000002,1.7924569,0.056422543,0.0016911370000000001,0.05787156,0.054050256,-0.33337316,0.013935573,-0.3163426,-0.35047740000000005
-15,110.0,0.0,156.0,1.0,156.0,3230.0,0.0,2.0,35.0,0.0,,,,,,,,,,,,0.62405765,0.9592649999999999,2.9572167,0.12969549,1.7828823,0.0024880429999999997,1.7908226999999999,1.7776691000000002,-0.06490173169544765,0.3036005258432666,1.5683243274688718,-0.3259851932525635,1.7211465,0.029713133,1.7807939999999998,1.678612,0.12291474,0.17745444,0.5574187,0.04589054,-0.112290025,0.43601707,0.9523574,-0.32423943
-16,122.0,0.0,238.0,1.0,238.0,3468.0,0.0,1.0,10.0,0.0,,,,,,,,,,,,0.15554756,0.054277197,0.29741687,0.08846552,1.7868587,0.0007967119500000001,1.7910342000000001,1.7857143,-0.1573395555669611,0.12615047772660626,0.7546746730804443,-0.3632258176803589,1.6729587,0.17595315,1.9944772000000002,1.502953,0.05232538,0.035234198,0.15977861,0.03124919,-0.28081256,0.079151474,-0.07227526599999999,-0.35482806
-17,132.0,0.0,185.0,1.0,185.0,3653.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,0.08596161,0.024157293,0.13088742,0.04359092,1.7865810000000002,0.0008728278999999999,1.7914173999999998,1.7849907999999999,-0.1145426501830419,0.06364297653654805,-0.008004307746887207,-0.26171672344207764,1.1001204,0.17392394,1.4450287,0.87743527,0.016055183,0.005272877,0.027343987,0.009870462,-0.20479403,0.033908524,-0.15814927,-0.26577490000000004
-18,156.0,0.0,469.0,1.0,469.0,4122.0,0.0,7.0,300.0,0.0,,,,,,,,,,,,0.44079409999999997,0.6836350999999999,2.4870617000000004,0.025862668,1.7806060000000001,0.0046418053,1.7915317,1.7732808999999998,0.062453726452329876,0.3429879814155853,0.985063374042511,-0.20165133476257324,0.92002445,0.10120275599999999,1.1328447,0.7651458999999999,0.091498,0.13105219999999998,0.42509866,0.007523017,0.11129719,0.47185699999999997,1.4228208,-0.21573834
-19,170.0,0.0,276.0,1.0,276.0,4398.0,0.0,3.0,20.0,0.0,,,,,,,,,,,,0.40178663,0.6728098000000001,2.4094729999999998,0.039225254,1.7645447,0.0042648454999999995,1.790634,1.7581539000000002,-0.00800227591624627,0.3189828889920542,1.685505986213684,-0.2216334342956543,1.1304478999999998,0.038905688,1.2024312,1.0620688999999999,0.0786909,0.1614253,0.58035713,0.012360237,-0.011329556999999999,0.4500582,1.2747773999999998,-0.24353555
-20,191.0,0.0,420.0,1.0,420.0,4818.0,0.0,7.0,315.0,0.0,,,,,,,,,,,,0.36499518,0.3705313,1.1575621,0.04431529,1.7636738,0.004579304,1.7901049,1.7550941999999998,0.021820819079875944,0.3071232093063115,0.947787582874298,-0.2100141644477844,0.9154280999999999,0.09096202,1.1273426000000002,0.75912726,0.07179129,0.09326242,0.24904189999999998,0.0062419563,0.040330485,0.34031707,0.7956849,-0.22417025
-21,197.0,0.0,104.0,1.0,104.0,4922.0,0.0,1.0,30.0,0.0,,,,,,,,,,,,0.5144350000000001,0.718648,1.9511981,0.12453204400000001,1.761423,0.0056095775,1.7902006999999998,1.7571856,0.05070284008979797,0.3168600697896934,0.925861954689026,-0.16295456886291504,0.88745534,0.012690918,0.93781537,0.8751633,0.06495949599999999,0.11300202,0.2909634,0.008293057,0.078482285,0.45015374,0.97864294,-0.15680452
-22,203.0,0.0,113.0,1.0,113.0,5035.0,0.0,2.0,15.0,0.0,,,,,,,,,,,,0.64118826,0.86793905,2.3767917,0.18172713,1.7537029999999998,0.006172906,1.7896771,1.749237,0.08747740507125855,0.4437113729234238,1.7083609104156494,-0.18463540077209475,0.9569305,0.024823021,1.0054495,0.92341274,0.12694135,0.23306239999999998,0.5930651,0.009591491,0.15546985,0.65049607,1.4562124,-0.19140014
-23,212.0,0.0,162.0,1.0,162.0,5197.0,0.0,1.0,5.0,0.0,,,,,,,,,,,,0.16368598,0.041053284,0.22597034,0.10290782,1.7606709999999999,0.004070151,1.7898455000000002,1.7559046999999999,-0.06407594718039036,0.13353017492122013,0.855490505695343,-0.17702490091323853,0.83308256,0.035228863,0.9194645,0.7807996,0.020336542,0.032399733,0.10603105,0.007155789,-0.11351252,0.08716212,0.11233470599999999,-0.18262672
-24,216.0,0.0,69.0,1.0,69.0,5266.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,0.18022996,0.014585165,0.19933702,0.16394733,1.7644328999999999,0.005773647,1.7902433999999998,1.7603636000000003,-0.07377323607603709,0.04112314147691277,-0.0068422555923461905,-0.1422249674797058,0.7145154,0.026194045,0.7514143,0.6838747,0.006449179300000001,0.00046015754999999996,0.006775117,0.0057984180000000005,-0.12904127,0.008062066,-0.118956625,-0.13869014
-25,238.0,0.0,432.0,1.0,432.0,5698.0,0.0,6.0,85.0,0.0,,,,,,,,,,,,0.33006436,0.35617912,1.3067303999999997,0.06890332,1.7717101999999998,0.004106822,1.7902195,1.7627416,0.07911585221687953,0.3249530771268533,0.9620689153671264,-0.13253813982009888,0.5261719,0.06930914,0.7088074000000001,0.44418199999999997,0.06108107,0.09490778,0.29277053,0.0023036576,0.140708,0.41082802,1.2361767,-0.12985662
+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
+1,0.0,1.0,248.0,1.0,248.0,248.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.8700310430476327,0.7405333778534565,2.2328769490220686,0.0,,,,,,,,,,,,,,,,,,,,
+2,0.0,1.0,123.0,1.0,123.0,371.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.3899670547558481,0.3819119128698883,1.0,0.0,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,88.0,1.0,88.0,459.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,187.0,1.0,187.0,646.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.7890979761079344,0.4557561638352592,1.6825545950103868,0.0,,,,,,,,,,,,,,,,,,,,
+5,0.0,1.0,86.0,1.0,86.0,732.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.6919135855443926,0.7222189379050715,1.8429431933839264,0.0,,,,,,,,,,,,,,,,,,,,
+6,0.0,1.0,331.0,1.0,331.0,1063.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.274934530443537,0.6459615236299282,2.6500575414623486,0.0,,,,,,,,,,,,,,,,,,,,
+7,19.0,0.0,397.0,1.0,397.0,1460.0,0.0,7.0,95.0,0.0,,,,,,,,,,,,0.13294017,0.26560363,0.95947796,0.00017420061,1.5215566513574144,0.8818366235165885,3.39289946333453,0.0,1.7916964,2.4401676e-05,1.7917565,1.7916276000000002,0.16945798030696765,0.4201839883771904,1.9003207683563232,-0.00874817930161953,0.009627631999999999,0.01605518,0.04531254,-0.012639573999999999,0.103175715,0.22770043,0.90946233,2.5499614e-07,0.30363909999999994,0.61167854,2.167231,-0.011617951000000001
+8,25.0,0.0,117.0,1.0,117.0,1577.0,0.0,1.0,15.0,0.0,,,,,,,,,,,,0.03459847,0.06251849,0.15962720000000002,0.0020247747,0.4872743474154752,0.33569100677546404,1.0,0.0,1.7916566000000003,4.586844e-05,1.7917542000000002,1.7916076,0.03243804715573788,0.19179319731839725,0.9877235293388368,-0.013366710394620895,0.06888939,0.0082683535,0.079700746,0.055720896,0.020763937,0.04136547,0.103494875,5.142918e-05,0.058279514000000004,0.14101307,0.34029025,-0.014587425
+9,40.0,0.0,290.0,1.0,290.0,1867.0,0.0,6.0,125.0,0.0,,,,,,,,,,,,0.197952,0.29690674,0.8451991,0.0032722908,1.5347843551661826,0.9835813996683812,3.292459852881254,0.0,1.7915696,8.654648000000001e-05,1.7917546999999998,1.7914047,0.20967606950019088,0.4340480766901692,1.8721247911453247,-0.03139287233352661,0.118590616,0.025857812,0.17259428,0.07850897,0.12374656,0.20499732,0.699171,0.000139082,0.37619528,0.61407316,1.7494822,-0.030527878999999997
+10,67.0,0.0,541.0,1.0,541.0,2408.0,0.0,10.0,150.0,0.0,,,,,,,,,,,,0.43392780000000003,0.69018596,2.5103388,0.012690847,1.5407734345932274,0.7374308486007165,3.1913177307618468,0.0,1.7898676,0.0019313908,1.7917521,1.7838042,0.1368366575584962,0.3821866825735704,1.6833410263061523,-0.2266237735748291,0.5796574999999999,0.29847825,1.2420191999999999,0.20709811,0.12345965,0.19106293,0.7305510000000001,0.00086669484,0.24575645,0.55341536,1.5624216,-0.22025344
+11,103.0,0.0,719.0,1.0,719.0,3127.0,0.0,4.0,265.0,0.0,,,,,,,,,,,,0.7696304,1.0534219999999999,4.5883107,0.20982262,0.5000529664376541,0.3155616701055577,1.2852839300228704,0.0,1.7330086999999998,0.032707155,1.7916646,1.6918119999999999,-0.1928262794017792,0.3110840395133576,0.9662883281707764,-0.7654213905334473,2.7413027000000003,0.87306994,4.0872965,1.4272719999999999,0.16251078,0.14811237,0.5942803000000001,0.04653442,-0.3311862,0.44391584,1.0693518999999998,-0.7295906999999999
+12,110.0,0.0,125.0,1.0,125.0,3252.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,0.64830524,0.14570446,0.8713232,0.40967187,0.0,0.0,0.0,0.0,1.7322674999999998,0.008331079,1.7875514,1.7221475,-0.3390433748563131,0.1860952368689896,-0.02489876747131348,-0.7148473262786865,3.3779035000000004,0.29400957,3.915662,3.0040746,0.102555595,0.017722348,0.13601841,0.08232693,-0.580518,0.079269625,-0.49676362,-0.73393583
+13,119.0,0.0,180.0,1.0,180.0,3432.0,0.0,2.0,15.0,0.0,,,,,,,,,,,,0.4787147,0.07543224,0.62356263,0.39566866,0.30227981965131145,0.5841033464651717,1.8345137614500877,0.0,1.6905703999999997,0.016943384,1.7857862,1.6741756,-0.2384866982698441,0.1775353277243849,0.5066013336181641,-0.5708379745483398,2.6603397999999996,0.29259694,3.1815927000000004,2.2532134,0.090995565,0.06636139,0.24196529999999997,0.04350890599999999,-0.41229284,0.08668608,-0.25544655,-0.5747671
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diff --git a/rl_coach/traces/Atari_A3C_breakout/trace.csv b/rl_coach/traces/Atari_A3C_breakout/trace.csv
index f0778d7..f32c1cd 100644
--- a/rl_coach/traces/Atari_A3C_breakout/trace.csv
+++ b/rl_coach/traces/Atari_A3C_breakout/trace.csv
@@ -1,196 +1,201 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
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diff --git a/rl_coach/traces/Atari_A3C_pong/trace.csv b/rl_coach/traces/Atari_A3C_pong/trace.csv
index 7f8a627..eb4c9d7 100644
--- a/rl_coach/traces/Atari_A3C_pong/trace.csv
+++ b/rl_coach/traces/Atari_A3C_pong/trace.csv
@@ -1,6 +1,6 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
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diff --git a/rl_coach/traces/Atari_A3C_space_invaders/trace.csv b/rl_coach/traces/Atari_A3C_space_invaders/trace.csv
index c723f0f..0898aa9 100644
--- a/rl_coach/traces/Atari_A3C_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_A3C_space_invaders/trace.csv
@@ -1,31 +1,31 @@
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diff --git a/rl_coach/traces/Atari_Bootstrapped_DQN_breakout/trace.csv b/rl_coach/traces/Atari_Bootstrapped_DQN_breakout/trace.csv
index 1f37755..3143865 100644
--- a/rl_coach/traces/Atari_Bootstrapped_DQN_breakout/trace.csv
+++ b/rl_coach/traces/Atari_Bootstrapped_DQN_breakout/trace.csv
@@ -1,206 +1,201 @@
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diff --git a/rl_coach/traces/Atari_Bootstrapped_DQN_pong/trace.csv b/rl_coach/traces/Atari_Bootstrapped_DQN_pong/trace.csv
index 5c6ea6c..7a45fbe 100644
--- a/rl_coach/traces/Atari_Bootstrapped_DQN_pong/trace.csv
+++ b/rl_coach/traces/Atari_Bootstrapped_DQN_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_Bootstrapped_DQN_space_invaders/trace.csv b/rl_coach/traces/Atari_Bootstrapped_DQN_space_invaders/trace.csv
index 9ef6f1f..4b84742 100644
--- a/rl_coach/traces/Atari_Bootstrapped_DQN_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_Bootstrapped_DQN_space_invaders/trace.csv
@@ -1,31 +1,26 @@
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diff --git a/rl_coach/traces/Atari_C51_breakout/trace.csv b/rl_coach/traces/Atari_C51_breakout/trace.csv
index 72c0fd0..17507f7 100644
--- a/rl_coach/traces/Atari_C51_breakout/trace.csv
+++ b/rl_coach/traces/Atari_C51_breakout/trace.csv
@@ -1,176 +1,176 @@
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diff --git a/rl_coach/traces/Atari_C51_pong/trace.csv b/rl_coach/traces/Atari_C51_pong/trace.csv
index b482a58..f7d1935 100644
--- a/rl_coach/traces/Atari_C51_pong/trace.csv
+++ b/rl_coach/traces/Atari_C51_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_C51_space_invaders/trace.csv b/rl_coach/traces/Atari_C51_space_invaders/trace.csv
index d125f7f..07a944a 100644
--- a/rl_coach/traces/Atari_C51_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_C51_space_invaders/trace.csv
@@ -1,26 +1,26 @@
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diff --git a/rl_coach/traces/Atari_DDQN_breakout/trace.csv b/rl_coach/traces/Atari_DDQN_breakout/trace.csv
index de7bdb7..d0ecbda 100644
--- a/rl_coach/traces/Atari_DDQN_breakout/trace.csv
+++ b/rl_coach/traces/Atari_DDQN_breakout/trace.csv
@@ -1,176 +1,176 @@
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diff --git a/rl_coach/traces/Atari_DDQN_pong/trace.csv b/rl_coach/traces/Atari_DDQN_pong/trace.csv
index 0b31cce..4b327ec 100644
--- a/rl_coach/traces/Atari_DDQN_pong/trace.csv
+++ b/rl_coach/traces/Atari_DDQN_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_DDQN_space_invaders/trace.csv b/rl_coach/traces/Atari_DDQN_space_invaders/trace.csv
index 877ef93..cca80ff 100644
--- a/rl_coach/traces/Atari_DDQN_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_DDQN_space_invaders/trace.csv
@@ -1,26 +1,26 @@
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diff --git a/rl_coach/traces/Atari_DDQN_with_PER_breakout/trace.csv b/rl_coach/traces/Atari_DDQN_with_PER_breakout/trace.csv
index 7ddbf09..8618756 100644
--- a/rl_coach/traces/Atari_DDQN_with_PER_breakout/trace.csv
+++ b/rl_coach/traces/Atari_DDQN_with_PER_breakout/trace.csv
@@ -1,211 +1,206 @@
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diff --git a/rl_coach/traces/Atari_DDQN_with_PER_pong/trace.csv b/rl_coach/traces/Atari_DDQN_with_PER_pong/trace.csv
index 6fabca0..055527a 100644
--- a/rl_coach/traces/Atari_DDQN_with_PER_pong/trace.csv
+++ b/rl_coach/traces/Atari_DDQN_with_PER_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_DDQN_with_PER_space_invaders/trace.csv b/rl_coach/traces/Atari_DDQN_with_PER_space_invaders/trace.csv
index fc5586a..5a8ca70 100644
--- a/rl_coach/traces/Atari_DDQN_with_PER_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_DDQN_with_PER_space_invaders/trace.csv
@@ -1,26 +1,26 @@
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diff --git a/rl_coach/traces/Atari_DQN_breakout/trace.csv b/rl_coach/traces/Atari_DQN_breakout/trace.csv
index 7f9e433..cdea50f 100644
--- a/rl_coach/traces/Atari_DQN_breakout/trace.csv
+++ b/rl_coach/traces/Atari_DQN_breakout/trace.csv
@@ -1,181 +1,181 @@
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diff --git a/rl_coach/traces/Atari_DQN_pong/trace.csv b/rl_coach/traces/Atari_DQN_pong/trace.csv
index 9c30971..bb4712e 100644
--- a/rl_coach/traces/Atari_DQN_pong/trace.csv
+++ b/rl_coach/traces/Atari_DQN_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_DQN_space_invaders/trace.csv b/rl_coach/traces/Atari_DQN_space_invaders/trace.csv
index 02effe2..6449eab 100644
--- a/rl_coach/traces/Atari_DQN_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_DQN_space_invaders/trace.csv
@@ -1,21 +1,26 @@
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diff --git a/rl_coach/traces/Atari_DQN_with_PER_breakout/trace.csv b/rl_coach/traces/Atari_DQN_with_PER_breakout/trace.csv
index 6336f42..b824901 100644
--- a/rl_coach/traces/Atari_DQN_with_PER_breakout/trace.csv
+++ b/rl_coach/traces/Atari_DQN_with_PER_breakout/trace.csv
@@ -1,206 +1,201 @@
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diff --git a/rl_coach/traces/Atari_DQN_with_PER_pong/trace.csv b/rl_coach/traces/Atari_DQN_with_PER_pong/trace.csv
index 4dd57a5..46dd704 100644
--- a/rl_coach/traces/Atari_DQN_with_PER_pong/trace.csv
+++ b/rl_coach/traces/Atari_DQN_with_PER_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_DQN_with_PER_space_invaders/trace.csv b/rl_coach/traces/Atari_DQN_with_PER_space_invaders/trace.csv
index 43355ee..349e703 100644
--- a/rl_coach/traces/Atari_DQN_with_PER_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_DQN_with_PER_space_invaders/trace.csv
@@ -1,26 +1,26 @@
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diff --git a/rl_coach/traces/Atari_Dueling_DDQN_breakout/trace.csv b/rl_coach/traces/Atari_Dueling_DDQN_breakout/trace.csv
index 3ea5c46..b1f8b05 100644
--- a/rl_coach/traces/Atari_Dueling_DDQN_breakout/trace.csv
+++ b/rl_coach/traces/Atari_Dueling_DDQN_breakout/trace.csv
@@ -1,176 +1,181 @@
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diff --git a/rl_coach/traces/Atari_Dueling_DDQN_pong/trace.csv b/rl_coach/traces/Atari_Dueling_DDQN_pong/trace.csv
index 65ad590..b053457 100644
--- a/rl_coach/traces/Atari_Dueling_DDQN_pong/trace.csv
+++ b/rl_coach/traces/Atari_Dueling_DDQN_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_Dueling_DDQN_space_invaders/trace.csv b/rl_coach/traces/Atari_Dueling_DDQN_space_invaders/trace.csv
index 1976ed8..7dbec36 100644
--- a/rl_coach/traces/Atari_Dueling_DDQN_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_Dueling_DDQN_space_invaders/trace.csv
@@ -1,26 +1,21 @@
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diff --git a/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_breakout/trace.csv b/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_breakout/trace.csv
index 54ef96d..804596a 100644
--- a/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_breakout/trace.csv
+++ b/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_breakout/trace.csv
@@ -1,201 +1,201 @@
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diff --git a/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_pong/trace.csv b/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_pong/trace.csv
index 1457c31..da248db 100644
--- a/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_pong/trace.csv
+++ b/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_space_invaders/trace.csv b/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_space_invaders/trace.csv
index 63dc133..2ac943c 100644
--- a/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_Dueling_DDQN_with_PER_OpenAI_space_invaders/trace.csv
@@ -1,26 +1,26 @@
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diff --git a/rl_coach/traces/Atari_NStepQ_breakout/trace.csv b/rl_coach/traces/Atari_NStepQ_breakout/trace.csv
index 232194c..303c467 100644
--- a/rl_coach/traces/Atari_NStepQ_breakout/trace.csv
+++ b/rl_coach/traces/Atari_NStepQ_breakout/trace.csv
@@ -1,176 +1,181 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,Q Values/Mean,Q Values/Stdev,Q Values/Max,Q Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min
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diff --git a/rl_coach/traces/Atari_NStepQ_pong/trace.csv b/rl_coach/traces/Atari_NStepQ_pong/trace.csv
index 520cd79..8e2f8f6 100644
--- a/rl_coach/traces/Atari_NStepQ_pong/trace.csv
+++ b/rl_coach/traces/Atari_NStepQ_pong/trace.csv
@@ -1,6 +1,6 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,Q Values/Mean,Q Values/Stdev,Q Values/Max,Q Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min
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-3,343.0,0.0,883.0,1.0,883.0,2834.0,0.4831733999999927,-20.0,-20.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,0.00039612237,0.022137828,0.047817677,-0.057933766,0.05449706,0.15011412,0.8670572,0.0013089271000000001
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diff --git a/rl_coach/traces/Atari_NStepQ_space_invaders/trace.csv b/rl_coach/traces/Atari_NStepQ_space_invaders/trace.csv
index 7a94a2c..1f42114 100644
--- a/rl_coach/traces/Atari_NStepQ_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_NStepQ_space_invaders/trace.csv
@@ -1,21 +1,26 @@
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diff --git a/rl_coach/traces/Atari_QR_DQN_breakout/trace.csv b/rl_coach/traces/Atari_QR_DQN_breakout/trace.csv
index 97ab7b7..9f49d2c 100644
--- a/rl_coach/traces/Atari_QR_DQN_breakout/trace.csv
+++ b/rl_coach/traces/Atari_QR_DQN_breakout/trace.csv
@@ -1,176 +1,176 @@
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diff --git a/rl_coach/traces/Atari_QR_DQN_pong/trace.csv b/rl_coach/traces/Atari_QR_DQN_pong/trace.csv
index 5296e21..e1d97b3 100644
--- a/rl_coach/traces/Atari_QR_DQN_pong/trace.csv
+++ b/rl_coach/traces/Atari_QR_DQN_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_QR_DQN_space_invaders/trace.csv b/rl_coach/traces/Atari_QR_DQN_space_invaders/trace.csv
index 5fc50a0..a6b5ab5 100644
--- a/rl_coach/traces/Atari_QR_DQN_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_QR_DQN_space_invaders/trace.csv
@@ -1,26 +1,26 @@
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diff --git a/rl_coach/traces/Atari_UCB_with_Q_Ensembles_breakout/trace.csv b/rl_coach/traces/Atari_UCB_with_Q_Ensembles_breakout/trace.csv
index a2a0644..c57bb8a 100644
--- a/rl_coach/traces/Atari_UCB_with_Q_Ensembles_breakout/trace.csv
+++ b/rl_coach/traces/Atari_UCB_with_Q_Ensembles_breakout/trace.csv
@@ -1,206 +1,206 @@
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diff --git a/rl_coach/traces/Atari_UCB_with_Q_Ensembles_pong/trace.csv b/rl_coach/traces/Atari_UCB_with_Q_Ensembles_pong/trace.csv
index 0eb9051..2a66f2e 100644
--- a/rl_coach/traces/Atari_UCB_with_Q_Ensembles_pong/trace.csv
+++ b/rl_coach/traces/Atari_UCB_with_Q_Ensembles_pong/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Atari_UCB_with_Q_Ensembles_space_invaders/trace.csv b/rl_coach/traces/Atari_UCB_with_Q_Ensembles_space_invaders/trace.csv
index 0bac314..fa9a94b 100644
--- a/rl_coach/traces/Atari_UCB_with_Q_Ensembles_space_invaders/trace.csv
+++ b/rl_coach/traces/Atari_UCB_with_Q_Ensembles_space_invaders/trace.csv
@@ -1,31 +1,26 @@
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diff --git a/rl_coach/traces/BitFlip_DQN/trace.csv b/rl_coach/traces/BitFlip_DQN/trace.csv
index a70a39b..be26a58 100644
--- a/rl_coach/traces/BitFlip_DQN/trace.csv
+++ b/rl_coach/traces/BitFlip_DQN/trace.csv
@@ -1,761 +1,766 @@
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diff --git a/rl_coach/traces/BitFlip_DQN_HER/trace.csv b/rl_coach/traces/BitFlip_DQN_HER/trace.csv
index 64e3712..37f232a 100644
--- a/rl_coach/traces/BitFlip_DQN_HER/trace.csv
+++ b/rl_coach/traces/BitFlip_DQN_HER/trace.csv
@@ -1,301 +1,301 @@
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diff --git a/rl_coach/traces/CartPole_A3C/trace.csv b/rl_coach/traces/CartPole_A3C/trace.csv
index 499a5a8..497cca6 100644
--- a/rl_coach/traces/CartPole_A3C/trace.csv
+++ b/rl_coach/traces/CartPole_A3C/trace.csv
@@ -1,221 +1,231 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
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diff --git a/rl_coach/traces/CartPole_DFP/trace.csv b/rl_coach/traces/CartPole_DFP/trace.csv
index fe357a7..b2b5e96 100644
--- a/rl_coach/traces/CartPole_DFP/trace.csv
+++ b/rl_coach/traces/CartPole_DFP/trace.csv
@@ -1,91 +1,101 @@
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+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min
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+2,0.0,1.0,29.0,2.0,16.0,29.0,0.5,,,0.0,,,,,,,,,,,,,,,,8.5,4.609772228646444,16.0,1.0
+3,0.0,1.0,56.0,3.0,27.0,56.0,0.5,,,0.0,,,,,,,,,,,,,,,,14.0,7.788880963698615,27.0,1.0
+4,0.0,1.0,67.0,4.0,11.0,67.0,0.5,,,0.0,,,,,,,,,,,,,,,,6.0,3.1622776601683795,11.0,1.0
+5,0.0,1.0,77.0,5.0,10.0,77.0,0.5,,,0.0,,,,,,,,,,,,,,,,5.5,2.8722813232690143,10.0,1.0
+6,0.0,1.0,94.0,6.0,17.0,94.0,0.5,,,0.0,,,,,,,,,,,,,,,,9.0,4.898979485566356,17.0,1.0
+7,0.0,1.0,106.0,7.0,12.0,106.0,0.5,,,0.0,,,,,,,,,,,,,,,,6.5,3.452052529534663,12.0,1.0
+8,0.0,1.0,121.0,8.0,15.0,121.0,0.5,,,0.0,,,,,,,,,,,,,,,,8.0,4.320493798938574,15.0,1.0
+9,0.0,1.0,138.0,9.0,17.0,138.0,0.5,,,0.0,,,,,,,,,,,,,,,,9.0,4.898979485566356,17.0,1.0
+10,0.0,1.0,172.0,10.0,34.0,172.0,0.5,,,0.0,,,,,,,,,,,,,,,,17.5,9.810708435174293,34.0,1.0
+11,0.0,1.0,187.0,11.0,15.0,187.0,0.5,,,0.0,,,,,,,,,,,,,,,,8.0,4.320493798938574,15.0,1.0
+12,0.0,1.0,201.0,12.0,14.0,201.0,0.5,,,0.0,,,,,,,,,,,,,,,,7.5,4.031128874149275,14.0,1.0
+13,0.0,1.0,218.0,13.0,17.0,218.0,0.5,,,0.0,,,,,,,,,,,,,,,,9.0,4.898979485566356,17.0,1.0
+14,0.0,1.0,235.0,14.0,17.0,235.0,0.5,,,0.0,,,,,,,,,,,,,,,,9.0,4.898979485566356,17.0,1.0
+15,0.0,1.0,262.0,15.0,27.0,262.0,0.5,,,0.0,,,,,,,,,,,,,,,,14.0,7.788880963698615,27.0,1.0
+16,0.0,1.0,279.0,16.0,17.0,279.0,0.5,,,0.0,,,,,,,,,,,,,,,,9.0,4.898979485566356,17.0,1.0
+17,0.0,1.0,330.0,17.0,51.0,330.0,0.5,,,0.0,,,,,,,,,,,,,,,,26.0,14.719601443879744,51.0,1.0
+18,0.0,1.0,350.0,18.0,20.0,350.0,0.5,,,0.0,,,,,,,,,,,,,,,,10.5,5.766281297335397,20.0,1.0
+19,0.0,1.0,402.0,19.0,52.0,402.0,0.5,,,0.0,,,,,,,,,,,,,,,,26.5,15.008331019803634,52.0,1.0
+20,0.0,1.0,411.0,20.0,9.0,411.0,0.5,,,0.0,,,,,,,,,,,,,,,,5.0,2.5819888974716108,9.0,1.0
+21,0.0,1.0,448.0,21.0,37.0,448.0,0.5,,,0.0,,,,,,,,,,,,,,,,19.0,10.677078252031311,37.0,1.0
+22,0.0,1.0,462.0,22.0,14.0,462.0,0.5,,,0.0,,,,,,,,,,,,,,,,7.5,4.031128874149275,14.0,1.0
+23,0.0,1.0,498.0,23.0,36.0,498.0,0.5,,,0.0,,,,,,,,,,,,,,,,18.5,10.388294694831616,36.0,1.0
+24,0.0,1.0,510.0,24.0,12.0,510.0,0.5,,,0.0,,,,,,,,,,,,,,,,6.5,3.452052529534663,12.0,1.0
+25,0.0,1.0,529.0,25.0,19.0,529.0,0.5,,,0.0,,,,,,,,,,,,,,,,10.0,5.47722557505166,19.0,1.0
+26,0.0,1.0,554.0,26.0,25.0,554.0,0.5,,,0.0,,,,,,,,,,,,,,,,13.0,7.211102550927977,25.0,1.0
+27,0.0,1.0,566.0,27.0,12.0,566.0,0.5,,,0.0,,,,,,,,,,,,,,,,6.5,3.452052529534663,12.0,1.0
+28,0.0,1.0,618.0,28.0,52.0,618.0,0.5,,,0.0,,,,,,,,,,,,,,,,26.5,15.008331019803634,52.0,1.0
+29,0.0,1.0,644.0,29.0,26.0,644.0,0.5,,,0.0,,,,,,,,,,,,,,,,13.5,7.5,26.0,1.0
+30,0.0,1.0,660.0,30.0,16.0,660.0,0.5,,,0.0,,,,,,,,,,,,,,,,8.5,4.609772228646444,16.0,1.0
+31,0.0,1.0,693.0,31.0,33.0,693.0,0.5,,,0.0,,,,,,,,,,,,,,,,17.0,9.521904571390467,33.0,1.0
+32,0.0,1.0,707.0,32.0,14.0,707.0,0.5,,,0.0,,,,,,,,,,,,,,,,7.5,4.031128874149275,14.0,1.0
+33,0.0,1.0,723.0,33.0,16.0,723.0,0.5,,,0.0,,,,,,,,,,,,,,,,8.5,4.609772228646444,16.0,1.0
+34,0.0,1.0,736.0,34.0,13.0,736.0,0.5,,,0.0,,,,,,,,,,,,,,,,7.0,3.741657386773941,13.0,1.0
+35,0.0,1.0,767.0,35.0,31.0,767.0,0.5,,,0.0,,,,,,,,,,,,,,,,16.0,8.94427190999916,31.0,1.0
+36,0.0,1.0,778.0,36.0,11.0,778.0,0.5,,,0.0,,,,,,,,,,,,,,,,6.0,3.1622776601683795,11.0,1.0
+37,0.0,1.0,800.0,37.0,22.0,800.0,0.5,,,0.0,,,,,,,,,,,,,,,,11.5,6.344288770224759,22.0,1.0
+38,0.0,1.0,825.0,38.0,25.0,825.0,0.5,,,0.0,,,,,,,,,,,,,,,,13.0,7.211102550927977,25.0,1.0
+39,0.0,1.0,845.0,39.0,20.0,845.0,0.5,,,0.0,,,,,,,,,,,,,,,,10.5,5.766281297335397,20.0,1.0
+40,0.0,1.0,867.0,40.0,22.0,867.0,0.5,,,0.0,,,,,,,,,,,,,,,,11.5,6.344288770224759,22.0,1.0
+41,0.0,1.0,916.0,41.0,49.0,916.0,0.5,,,0.0,,,,,,,,,,,,,,,,25.0,14.142135623730953,49.0,1.0
+42,0.0,1.0,930.0,42.0,14.0,930.0,0.5,,,0.0,,,,,,,,,,,,,,,,7.5,4.031128874149275,14.0,1.0
+43,0.0,1.0,952.0,43.0,22.0,952.0,0.5,,,0.0,,,,,,,,,,,,,,,,11.5,6.344288770224759,22.0,1.0
+44,0.0,1.0,964.0,44.0,12.0,964.0,0.5,,,0.0,,,,,,,,,,,,,,,,6.5,3.452052529534663,12.0,1.0
+45,0.0,1.0,984.0,45.0,20.0,984.0,0.5,,,0.0,,,,,,,,,,,,,,,,10.5,5.766281297335397,20.0,1.0
+46,0.0,1.0,994.0,46.0,10.0,994.0,0.5,,,0.0,,,,,,,,,,,,,,,,5.5,2.8722813232690143,10.0,1.0
+47,0.0,1.0,1011.0,47.0,17.0,1011.0,0.5,,,0.0,,,,,,,,,,,,,,,,9.0,4.898979485566356,17.0,1.0
+48,0.0,1.0,1043.0,48.0,32.0,1043.0,0.5,,,0.0,,,,,,,,,,,,,,,,16.5,9.233092656309692,32.0,1.0
+49,11.0,0.0,1055.0,49.0,12.0,1055.0,0.4980399999999998,12.0,12.0,0.0,,,,385.6464177911932,56.4382435744357,453.7681884765625,298.00506591796875,0.0001,0.0,0.0001,0.0001,368.2701,70.72579,465.1204,242.56297,6.5,3.452052529534663,12.0,1.0
+50,37.0,0.0,1081.0,50.0,26.0,1081.0,0.4937933333333328,26.0,26.0,0.0,,,,330.9097998046875,53.81167843305303,446.1451721191406,256.64093017578125,0.0001,0.0,0.0001,0.0001,233.26672000000002,103.295525,490.76437000000004,69.08931,13.5,7.5,26.0,1.0
+51,53.0,0.0,1097.0,51.0,16.0,1097.0,0.4911799999999992,16.0,16.0,0.0,,,,296.7834452311198,51.609968830357616,383.2273254394531,230.49615478515625,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,141.60435,52.660140000000006,272.00327000000004,87.53429399999999,8.5,4.609772228646444,16.0,1.0
+52,64.0,0.0,1108.0,52.0,11.0,1108.0,0.4893833333333323,11.0,11.0,0.0,,,,278.450424194336,44.172208999466505,366.86895751953125,223.49508666992188,0.0001,0.0,0.0001,0.0001,168.62126,82.076256,328.95718,68.28081999999999,6.0,3.1622776601683795,11.0,1.0
+53,87.0,0.0,1131.0,53.0,23.0,1131.0,0.4856266666666653,23.0,23.0,0.0,,,,279.5792805064808,44.463016738307054,368.356689453125,192.03564453125,0.0001,1.3552527156068802e-20,0.0001,0.0001,156.4273,67.1064,302.95444,77.61362,12.0,6.6332495807108,23.0,1.0
+54,152.0,0.0,1196.0,54.0,65.0,1196.0,0.4750099999999977,65.0,65.0,0.0,,,,264.7044055461884,43.085217005025214,366.64361572265625,169.93707275390625,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,134.45761000000002,54.68561999999999,364.3526,63.73161700000001,33.0,18.76166303929372,65.0,1.0
+55,189.0,0.0,1233.0,55.0,37.0,1233.0,0.4689666666666638,37.0,37.0,0.0,,,,255.27068540785046,33.441591554475,313.8843688964844,171.51336669921875,0.0001,1.3552527156068802e-20,0.0001,0.0001,143.91646,58.998302,367.70074,82.520676,19.0,10.677078252031311,37.0,1.0
+56,211.0,0.0,1255.0,56.0,22.0,1255.0,0.4653733333333301,22.0,22.0,0.0,,,,221.88248189290368,33.96892124492252,294.3837890625,164.7964324951172,0.0001,1.3552527156068802e-20,0.0001,0.0001,134.08221,35.01956,226.8712,87.9466,11.5,6.344288770224759,22.0,1.0
+57,234.0,0.0,1278.0,57.0,23.0,1278.0,0.4616166666666631,23.0,23.0,0.0,,,,221.29095736416903,33.47633571662085,299.88482666015625,173.60752868652344,0.0001,1.3552527156068802e-20,0.0001,0.0001,151.41378999999998,49.70632,316.69745,90.079544,12.0,6.6332495807108,23.0,1.0
+58,272.0,0.0,1316.0,58.0,38.0,1316.0,0.4554099999999958,38.0,38.0,0.0,,,,197.78244307234482,25.446432524884266,240.49594116210938,152.11749267578125,9.999999999999998e-05,2.7105054312137605e-20,0.0001,0.0001,155.72125,38.629005,252.9965,90.86801,19.5,10.965856099730654,38.0,1.0
+59,300.0,0.0,1344.0,59.0,28.0,1344.0,0.4508366666666621,28.0,28.0,0.0,,,,172.4388269495081,24.86754993610045,224.26959228515625,130.49940490722656,0.0001,1.3552527156068802e-20,0.0001,0.0001,150.00925,46.7189,307.2137,88.17799000000001,14.5,8.077747210701755,28.0,1.0
+60,343.0,0.0,1387.0,60.0,43.0,1387.0,0.4438133333333281,43.0,43.0,0.0,,,,163.51902807326545,22.510460367934094,235.01145935058597,125.7556610107422,0.00010000000000000002,1.3552527156068802e-20,0.0001,0.0001,159.09685,47.885742,306.78925,61.879234,22.0,12.409673645990855,43.0,1.0
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diff --git a/rl_coach/traces/CartPole_DQN/trace.csv b/rl_coach/traces/CartPole_DQN/trace.csv
index f33a0b2..7edc52b 100644
--- a/rl_coach/traces/CartPole_DQN/trace.csv
+++ b/rl_coach/traces/CartPole_DQN/trace.csv
@@ -1,171 +1,171 @@
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diff --git a/rl_coach/traces/CartPole_Dueling_DDQN/trace.csv b/rl_coach/traces/CartPole_Dueling_DDQN/trace.csv
index 598302d..037857a 100644
--- a/rl_coach/traces/CartPole_Dueling_DDQN/trace.csv
+++ b/rl_coach/traces/CartPole_Dueling_DDQN/trace.csv
@@ -1,186 +1,181 @@
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diff --git a/rl_coach/traces/CartPole_NStepQ/trace.csv b/rl_coach/traces/CartPole_NStepQ/trace.csv
index 203a713..60f476a 100644
--- a/rl_coach/traces/CartPole_NStepQ/trace.csv
+++ b/rl_coach/traces/CartPole_NStepQ/trace.csv
@@ -1,191 +1,166 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,Q Values/Mean,Q Values/Stdev,Q Values/Max,Q Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min
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diff --git a/rl_coach/traces/CartPole_PAL/trace.csv b/rl_coach/traces/CartPole_PAL/trace.csv
index 153d7fe..bab23cb 100644
--- a/rl_coach/traces/CartPole_PAL/trace.csv
+++ b/rl_coach/traces/CartPole_PAL/trace.csv
@@ -1,171 +1,161 @@
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diff --git a/rl_coach/traces/CartPole_PG/trace.csv b/rl_coach/traces/CartPole_PG/trace.csv
index 1d7eb14..74136f5 100644
--- a/rl_coach/traces/CartPole_PG/trace.csv
+++ b/rl_coach/traces/CartPole_PG/trace.csv
@@ -1,226 +1,216 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Returns Mean/Mean,Returns Mean/Stdev,Returns Mean/Max,Returns Mean/Min,Returns Variance/Mean,Returns Variance/Stdev,Returns Variance/Max,Returns Variance/Min
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-51,2.0,0.0,13.0,1.0,13.0,1104.0,0.0,0.06499999999999999,13.0,0.0,,,,,,,,,,,,,,,,0.69280154,0.00029581970000000003,0.6931421999999999,0.69217277,,,,,,,,
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-55,6.0,0.0,19.0,1.0,19.0,1212.0,0.0,0.095,19.0,0.0,,,,,,,,,,,,,,,,0.69280386,0.00042367526,0.69314516,0.6918092,,,,,,,,
-56,7.0,0.0,12.0,1.0,12.0,1224.0,0.0,0.05999999999999999,12.0,0.0,,,,,,,,,,,,,,,,0.6922489000000001,0.000640234,0.6931435,0.69108003,,,,,,,,
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-193,144.0,0.0,34.0,1.0,34.0,4853.0,0.0,0.17000000000000007,34.0,0.0,,,,,,,,,,,,,,,,0.6779479,0.016356846,0.6931468000000001,0.6400794,,,,,,,,
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-195,146.0,0.0,24.0,1.0,24.0,4904.0,0.0,0.12000000000000005,24.0,0.0,,,,,,,,,,,,,,,,0.67715484,0.012998386,0.6929194000000001,0.65071726,,,,,,,,
-196,147.0,0.0,54.0,1.0,54.0,4958.0,0.0,0.2700000000000001,54.0,0.0,,,,,,,,,,,,,,,,0.6733789000000001,0.021477655,0.6931416,0.58913064,,,,,,,,
-197,148.0,0.0,37.0,1.0,37.0,4995.0,0.0,0.18500000000000008,37.0,0.0,,,,,,,,,,,,,,,,0.6610391,0.035325937,0.69288963,0.5452045,,,,,,,,
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-206,157.0,0.0,60.0,1.0,60.0,5269.0,0.0,0.30000000000000016,60.0,0.0,,,,,,,,,,,,,,,,0.66102046,0.03468346,0.6931438,0.5435293999999999,,,,,,,,
-207,158.0,0.0,22.0,1.0,22.0,5291.0,0.0,0.11000000000000004,22.0,0.0,,,,,,,,,,,,,,,,0.65481776,0.045677915,0.6931404999999999,0.52198416,,,,,,,,
-208,159.0,0.0,27.0,1.0,27.0,5318.0,0.0,0.13500000000000004,27.0,0.0,,,,,,,,,,,,,,,,0.6579552,0.03341417,0.6929232,0.54441136,,,,,,,,
-209,160.0,0.0,51.0,1.0,51.0,5369.0,0.0,0.2550000000000001,51.0,0.0,,,,,,,,,,,,,,,,0.66670144,0.030817755,0.6930961999999999,0.5648303000000001,,,,,,,,
-210,161.0,0.0,21.0,1.0,21.0,5390.0,0.0,0.10500000000000002,21.0,0.0,,,,,,,,,,,,,,,,0.6228243,0.08343502,0.6928420999999999,0.43418086,,,,,,,,
-211,162.0,0.0,59.0,1.0,59.0,5449.0,0.0,0.2950000000000001,59.0,0.0,,,,,,,,,,,,,,,,0.6695004,0.019584788,0.6929871999999999,0.617161,,,,,,,,
-212,163.0,0.0,69.0,1.0,69.0,5518.0,0.0,0.3450000000000002,69.0,0.0,,,,,,,,,,,,,,,,0.67028844,0.023152169,0.6931449000000001,0.5864078,,,,,,,,
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-214,165.0,0.0,15.0,1.0,15.0,5591.0,0.0,0.075,15.0,0.0,,,,,,,,,,,,,,,,0.6300255,0.049573146,0.6919417,0.52256244,,,,,,,,
-215,166.0,0.0,30.0,1.0,30.0,5621.0,0.0,0.15000000000000005,30.0,0.0,,,,,,,,,,,,,,,,0.6636754,0.034410782,0.6930824999999999,0.5490352,,,,,,,,
-216,167.0,0.0,48.0,1.0,48.0,5669.0,0.0,0.2400000000000001,48.0,0.0,,,,,,,,,,,,,,,,0.6591225000000001,0.028648003999999998,0.69314325,0.5806420999999999,,,,,,,,
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-218,169.0,0.0,35.0,1.0,35.0,5726.0,0.0,0.17500000000000007,35.0,0.0,,,,,,,,,,,,,,,,0.66869885,0.02504079,0.6931467,0.58864474,,,,,,,,
-219,170.0,0.0,21.0,1.0,21.0,5747.0,0.0,0.10500000000000002,21.0,0.0,,,,,,,,,,,,,,,,0.65570045,0.035889952999999995,0.69314533,0.5742477,,,,,,,,
-220,171.0,0.0,68.0,1.0,68.0,5815.0,0.0,0.3400000000000002,68.0,0.0,,,,,,,,,,,,,,,,0.67377526,0.023752997,0.69314575,0.5704627,,,,,,,,
-221,172.0,0.0,12.0,1.0,12.0,5827.0,0.0,0.05999999999999999,12.0,0.0,,,,,,,,,,,,,,,,0.6313793000000001,0.03832572,0.6928804000000001,0.547421,,,,,,,,
-222,173.0,0.0,58.0,1.0,58.0,5885.0,0.0,0.2900000000000001,58.0,0.0,,,,,,,,,,,,,,,,0.66505045,0.028303195,0.6931279,0.55157405,,,,,,,,
-223,174.0,0.0,12.0,1.0,12.0,5897.0,0.0,0.05999999999999999,12.0,0.0,,,,,,,,,,,,,,,,0.63177973,0.036184084,0.6927339,0.55872023,,,,,,,,
-224,175.0,0.0,42.0,1.0,42.0,5939.0,0.0,0.2100000000000001,42.0,0.0,,,,,,,,,,,,,,,,0.6655469,0.033985395,0.6931454,0.539849,,,,,,,,
-225,176.0,0.0,29.0,1.0,29.0,5968.0,0.0,0.14500000000000005,29.0,0.0,,,,,,,,,,,,,,,,0.6608282,0.035957467,0.6931467,0.55187505,,,,,,,,
+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Returns Mean/Mean,Returns Mean/Stdev,Returns Mean/Max,Returns Mean/Min,Returns Variance/Mean,Returns Variance/Stdev,Returns Variance/Max,Returns Variance/Min
+1,0.0,1.0,15.0,1.0,15.0,15.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.03819257031625213,0.02004889587224851,0.06997082267935574,0.005,,,,,,,,,,,,
+2,0.0,1.0,16.0,1.0,16.0,31.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.04044747932477151,0.02128626449475752,0.07427111445256218,0.005,,,,,,,,,,,,
+3,0.0,1.0,27.0,1.0,27.0,58.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.06429497630302354,0.0340862968900576,0.11882864282644813,0.005,,,,,,,,,,,,
+4,0.0,1.0,11.0,1.0,11.0,69.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.029022144164224006,0.014966996075313928,0.052330872870641774,0.005,,,,,,,,,,,,
+5,0.0,1.0,10.0,1.0,10.0,79.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.02669127129358223,0.013661969731348278,0.04780896249559776,0.005,,,,,,,,,,,,
+6,0.0,1.0,17.0,1.0,17.0,96.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.042687533676728284,0.02251082658499651,0.07852840330803658,0.005,,,,,,,,,,,,
+7,0.0,1.0,12.0,1.0,12.0,108.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.03133759582903328,0.016257855673222694,0.056807564141935364,0.005,,,,,,,,,,,,
+8,0.0,1.0,15.0,1.0,15.0,123.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.03819257031625213,0.02004889587224851,0.06997082267935574,0.005,,,,,,,,,,,,
+9,0.0,1.0,17.0,1.0,17.0,140.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.042687533676728284,0.02251082658499651,0.07852840330803658,0.005,,,,,,,,,,,,
+10,0.0,1.0,34.0,1.0,34.0,174.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.07859955146995502,0.041509921651436785,0.1447233863638538,0.005,,,,,,,,,,,,
+11,0.0,1.0,15.0,1.0,15.0,189.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.03819257031625213,0.02004889587224851,0.06997082267935574,0.005,,,,,,,,,,,,
+12,0.0,1.0,14.0,1.0,14.0,203.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.035922695147459015,0.01879852463102244,0.06562709361551085,0.005,,,,,,,,,,,,
+13,0.0,1.0,17.0,1.0,17.0,220.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.042687533676728284,0.02251082658499651,0.07852840330803658,0.005,,,,,,,,,,,,
+14,0.0,1.0,17.0,1.0,17.0,237.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.042687533676728284,0.02251082658499651,0.07852840330803658,0.005,,,,,,,,,,,,
+15,0.0,1.0,27.0,1.0,27.0,264.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.06429497630302354,0.0340862968900576,0.11882864282644813,0.005,,,,,,,,,,,,
+16,0.0,1.0,17.0,1.0,17.0,281.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.042687533676728284,0.02251082658499651,0.07852840330803658,0.005,,,,,,,,,,,,
+17,0.0,1.0,51.0,1.0,51.0,332.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.11075141804068593,0.05745852664846605,0.2005219967669193,0.005,,,,,,,,,,,,
+18,0.0,1.0,20.0,1.0,20.0,352.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.049319670553146414,0.02610938697149644,0.09104653120138456,0.005,,,,,,,,,,,,
+19,0.0,1.0,52.0,1.0,52.0,404.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.11253536724758137,0.05831207811499739,0.20351677679925012,0.005,,,,,,,,,,,,
+20,0.0,1.0,9.0,1.0,9.0,413.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.024344861160024947,0.0123422783916159,0.04324137625817955,0.005,,,,,,,,,,,,
+21,0.0,1.0,37.0,1.0,37.0,450.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.08453323650052327,0.04453153900608736,0.15527545706546098,0.005,,,,,,,,,,,,
+22,0.0,1.0,14.0,1.0,14.0,464.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.035922695147459015,0.01879852463102244,0.06562709361551085,0.005,,,,,,,,,,,,
+23,0.0,1.0,36.0,1.0,36.0,500.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.08256817481816388,0.04353468290208385,0.1517933909752131,0.005,,,,,,,,,,,,
+24,0.0,1.0,12.0,1.0,12.0,512.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.03133759582903328,0.016257855673222694,0.056807564141935364,0.005,,,,,,,,,,,,
+25,0.0,1.0,19.0,1.0,19.0,531.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.04712351999271282,0.024922228920121338,0.08691568808220664,0.005,,,,,,,,,,,,
+26,0.0,1.0,25.0,1.0,25.0,556.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.060086291610310615,0.031865394096605694,0.1110893203004266,0.005,,,,,,,,,,,,
+27,0.0,1.0,12.0,1.0,12.0,568.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.03133759582903328,0.016257855673222694,0.056807564141935364,0.005,,,,,,,,,,,,
+28,0.0,1.0,52.0,1.0,52.0,620.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.11253536724758137,0.05831207811499739,0.20351677679925012,0.005,,,,,,,,,,,,
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diff --git a/rl_coach/traces/ControlSuite_DDPG_cartpole_swingup/trace.csv b/rl_coach/traces/ControlSuite_DDPG_cartpole_swingup/trace.csv
index 7f33529..6729933 100644
--- a/rl_coach/traces/ControlSuite_DDPG_cartpole_swingup/trace.csv
+++ b/rl_coach/traces/ControlSuite_DDPG_cartpole_swingup/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/ControlSuite_DDPG_hopper_hop/trace.csv b/rl_coach/traces/ControlSuite_DDPG_hopper_hop/trace.csv
index 4d86bd9..b457fdd 100644
--- a/rl_coach/traces/ControlSuite_DDPG_hopper_hop/trace.csv
+++ b/rl_coach/traces/ControlSuite_DDPG_hopper_hop/trace.csv
@@ -1,6 +1,6 @@
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diff --git a/rl_coach/traces/Doom_Basic_A3C/trace.csv b/rl_coach/traces/Doom_Basic_A3C/trace.csv
index 24c6ee6..8dc5be0 100644
--- a/rl_coach/traces/Doom_Basic_A3C/trace.csv
+++ b/rl_coach/traces/Doom_Basic_A3C/trace.csv
@@ -1,136 +1,121 @@
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diff --git a/rl_coach/traces/Doom_Basic_DFP/trace.csv b/rl_coach/traces/Doom_Basic_DFP/trace.csv
index ba2fb5a..0b0be8a 100644
--- a/rl_coach/traces/Doom_Basic_DFP/trace.csv
+++ b/rl_coach/traces/Doom_Basic_DFP/trace.csv
@@ -1,66 +1,66 @@
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diff --git a/rl_coach/traces/Doom_Basic_DQN/trace.csv b/rl_coach/traces/Doom_Basic_DQN/trace.csv
index 119bda7..1792ac0 100644
--- a/rl_coach/traces/Doom_Basic_DQN/trace.csv
+++ b/rl_coach/traces/Doom_Basic_DQN/trace.csv
@@ -1,156 +1,111 @@
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diff --git a/rl_coach/traces/Doom_Basic_Dueling_DDQN/trace.csv b/rl_coach/traces/Doom_Basic_Dueling_DDQN/trace.csv
index bdc2693..a273d4b 100644
--- a/rl_coach/traces/Doom_Basic_Dueling_DDQN/trace.csv
+++ b/rl_coach/traces/Doom_Basic_Dueling_DDQN/trace.csv
@@ -1,156 +1,136 @@
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diff --git a/rl_coach/traces/Doom_Battle_DFP/trace.csv b/rl_coach/traces/Doom_Battle_DFP/trace.csv
index 0e860e1..c483252 100644
--- a/rl_coach/traces/Doom_Battle_DFP/trace.csv
+++ b/rl_coach/traces/Doom_Battle_DFP/trace.csv
@@ -1,56 +1,56 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min
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diff --git a/rl_coach/traces/ExplorationChain_Bootstrapped_DQN/trace.csv b/rl_coach/traces/ExplorationChain_Bootstrapped_DQN/trace.csv
index ab7f4da..cd4b3d7 100644
--- a/rl_coach/traces/ExplorationChain_Bootstrapped_DQN/trace.csv
+++ b/rl_coach/traces/ExplorationChain_Bootstrapped_DQN/trace.csv
@@ -1,221 +1,221 @@
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diff --git a/rl_coach/traces/ExplorationChain_Dueling_DDQN/trace.csv b/rl_coach/traces/ExplorationChain_Dueling_DDQN/trace.csv
index 925a75d..23e2e3e 100644
--- a/rl_coach/traces/ExplorationChain_Dueling_DDQN/trace.csv
+++ b/rl_coach/traces/ExplorationChain_Dueling_DDQN/trace.csv
@@ -1,221 +1,221 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min
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diff --git a/rl_coach/traces/ExplorationChain_UCB_Q_ensembles/trace.csv b/rl_coach/traces/ExplorationChain_UCB_Q_ensembles/trace.csv
index acf58ae..0321455 100644
--- a/rl_coach/traces/ExplorationChain_UCB_Q_ensembles/trace.csv
+++ b/rl_coach/traces/ExplorationChain_UCB_Q_ensembles/trace.csv
@@ -1,221 +1,221 @@
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+2,0.0,1.0,54.0,54.0,27.0,54.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.004735183979535818,0.003231659794164425,0.008452495392719559,0.0,,,,
+3,0.0,1.0,81.0,81.0,27.0,81.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0021999809223264308,0.0012275070214652073,0.004450510253506932,0.0,,,,
+4,0.0,1.0,108.0,108.0,27.0,108.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.00380341850713044,0.002506325356001919,0.00805361313441897,0.0,,,,
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+6,0.0,1.0,162.0,162.0,27.0,162.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.00011074074074074077,0.00041405784106523033,0.00199,0.0,,,,
+7,0.0,1.0,189.0,189.0,27.0,189.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.002997589325143781,0.0012147224539993196,0.003940399,0.0,,,,
+8,0.0,1.0,216.0,216.0,27.0,216.0,0.0,,,0.0,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,,,,
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diff --git a/rl_coach/traces/Fetch_DDPG_HER_baselines_pick_and_place/trace.csv b/rl_coach/traces/Fetch_DDPG_HER_baselines_pick_and_place/trace.csv
index 96956e7..2925e3c 100644
--- a/rl_coach/traces/Fetch_DDPG_HER_baselines_pick_and_place/trace.csv
+++ b/rl_coach/traces/Fetch_DDPG_HER_baselines_pick_and_place/trace.csv
@@ -1,121 +1,121 @@
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diff --git a/rl_coach/traces/Fetch_DDPG_HER_baselines_push/trace.csv b/rl_coach/traces/Fetch_DDPG_HER_baselines_push/trace.csv
index e5e74cd..028c8dc 100644
--- a/rl_coach/traces/Fetch_DDPG_HER_baselines_push/trace.csv
+++ b/rl_coach/traces/Fetch_DDPG_HER_baselines_push/trace.csv
@@ -1,121 +1,121 @@
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diff --git a/rl_coach/traces/Fetch_DDPG_HER_baselines_reach/trace.csv b/rl_coach/traces/Fetch_DDPG_HER_baselines_reach/trace.csv
index f0afc8a..77acc2f 100644
--- a/rl_coach/traces/Fetch_DDPG_HER_baselines_reach/trace.csv
+++ b/rl_coach/traces/Fetch_DDPG_HER_baselines_reach/trace.csv
@@ -1,121 +1,121 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,TD targets/Mean,TD targets/Stdev,TD targets/Max,TD targets/Min,actions/Mean,actions/Stdev,actions/Max,actions/Min
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diff --git a/rl_coach/traces/Fetch_DDPG_HER_baselines_slide/trace.csv b/rl_coach/traces/Fetch_DDPG_HER_baselines_slide/trace.csv
index f7d2859..e8d7876 100644
--- a/rl_coach/traces/Fetch_DDPG_HER_baselines_slide/trace.csv
+++ b/rl_coach/traces/Fetch_DDPG_HER_baselines_slide/trace.csv
@@ -1,121 +1,121 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,TD targets/Mean,TD targets/Stdev,TD targets/Max,TD targets/Min,actions/Mean,actions/Stdev,actions/Max,actions/Min
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+2,0.0,1.0,492.0,2.0,50.0,100.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,738.0,3.0,50.0,150.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,984.0,4.0,50.0,200.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,0.0,1.0,1230.0,5.0,50.0,250.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,0.0,1.0,1476.0,6.0,50.0,300.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,1722.0,7.0,50.0,350.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,0.0,1.0,1968.0,8.0,50.0,400.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+10,0.0,1.0,2460.0,10.0,50.0,500.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,0.0,1.0,2706.0,11.0,50.0,550.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+12,0.0,1.0,2952.0,12.0,50.0,600.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,0.0,1.0,3198.0,13.0,50.0,650.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+14,0.0,1.0,3444.0,14.0,50.0,700.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,0.0,1.0,3690.0,15.0,50.0,750.0,0.1,,,0.0,,,,,,,,,,,,,,,,-18.84431432426873,9.007305717332294,-1.0,-31.79151599564413,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/InvertedPendulum_PG/trace.csv b/rl_coach/traces/InvertedPendulum_PG/trace.csv
index 195d87b..8adc912 100644
--- a/rl_coach/traces/InvertedPendulum_PG/trace.csv
+++ b/rl_coach/traces/InvertedPendulum_PG/trace.csv
@@ -1,351 +1,261 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Returns Mean/Mean,Returns Mean/Stdev,Returns Mean/Max,Returns Mean/Min,Returns Variance/Mean,Returns Variance/Stdev,Returns Variance/Max,Returns Variance/Min
-1,0.0,1.0,4.0,1.0,4.0,4.0,0.1,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-53,0.0,1.0,4.0,1.0,4.0,306.0,0.1,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-54,0.0,1.0,7.0,1.0,7.0,313.0,0.1,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-56,0.0,1.0,4.0,1.0,4.0,320.0,0.1,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-58,0.0,1.0,4.0,1.0,4.0,329.0,0.1,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-59,0.0,1.0,4.0,1.0,4.0,333.0,0.1,,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-309,127.0,0.0,61.0,1.0,61.0,3084.0,0.1,3.049999999999997,61.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-318,136.0,0.0,44.0,1.0,44.0,3616.0,0.1,2.2,44.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-319,137.0,0.0,107.0,1.0,107.0,3723.0,0.1,5.349999999999988,107.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-321,139.0,0.0,114.0,1.0,114.0,3893.0,0.1,5.699999999999987,114.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-322,140.0,0.0,63.0,1.0,63.0,3956.0,0.1,3.149999999999997,63.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-323,141.0,0.0,47.0,1.0,47.0,4003.0,0.1,2.35,47.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-324,142.0,0.0,48.0,1.0,48.0,4051.0,0.1,2.3999999999999995,48.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-325,143.0,0.0,59.0,1.0,59.0,4110.0,0.1,2.949999999999997,59.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-326,144.0,0.0,142.0,1.0,142.0,4252.0,0.1,7.099999999999982,142.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-327,145.0,0.0,83.0,1.0,83.0,4335.0,0.1,4.149999999999993,83.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-328,146.0,0.0,57.0,1.0,57.0,4392.0,0.1,2.8499999999999983,57.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-329,147.0,0.0,55.0,1.0,55.0,4447.0,0.1,2.7499999999999982,55.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-330,148.0,0.0,47.0,1.0,47.0,4494.0,0.1,2.35,47.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-331,149.0,0.0,57.0,1.0,57.0,4551.0,0.1,2.8499999999999983,57.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-332,150.0,0.0,61.0,1.0,61.0,4612.0,0.1,3.049999999999997,61.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-333,151.0,0.0,80.0,1.0,80.0,4692.0,0.1,3.9999999999999933,80.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-334,152.0,0.0,59.0,1.0,59.0,4751.0,0.1,2.949999999999997,59.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-335,153.0,0.0,58.0,1.0,58.0,4809.0,0.1,2.8999999999999977,58.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-336,154.0,0.0,87.0,1.0,87.0,4896.0,0.1,4.3499999999999925,87.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-338,156.0,0.0,57.0,1.0,57.0,5039.0,0.1,2.8499999999999983,57.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-339,157.0,0.0,67.0,1.0,67.0,5106.0,0.1,3.3499999999999956,67.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-340,158.0,0.0,57.0,1.0,57.0,5163.0,0.1,2.8499999999999983,57.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-341,159.0,0.0,102.0,1.0,102.0,5265.0,0.1,5.099999999999991,102.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-342,160.0,0.0,59.0,1.0,59.0,5324.0,0.1,2.949999999999997,59.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-343,161.0,0.0,68.0,1.0,68.0,5392.0,0.1,3.399999999999996,68.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-344,162.0,0.0,55.0,1.0,55.0,5447.0,0.1,2.7499999999999982,55.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-345,163.0,0.0,65.0,1.0,65.0,5512.0,0.1,3.2499999999999964,65.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-346,164.0,0.0,68.0,1.0,68.0,5580.0,0.1,3.399999999999996,68.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-347,165.0,0.0,47.0,1.0,47.0,5627.0,0.1,2.35,47.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-348,166.0,0.0,42.0,1.0,42.0,5669.0,0.1,2.1000000000000005,42.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-349,167.0,0.0,56.0,1.0,56.0,5725.0,0.1,2.799999999999998,56.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-350,168.0,0.0,67.0,1.0,67.0,5792.0,0.1,3.3499999999999956,67.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Returns Mean/Mean,Returns Mean/Stdev,Returns Mean/Max,Returns Mean/Min,Returns Variance/Mean,Returns Variance/Stdev,Returns Variance/Max,Returns Variance/Min
+1,0.0,1.0,4.0,1.0,4.0,4.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+2,0.0,1.0,3.0,1.0,3.0,7.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+3,0.0,1.0,3.0,1.0,3.0,10.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+4,0.0,1.0,7.0,1.0,7.0,17.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.19604960199429286,0.09655815980645327,0.33967326046505,0.05,,,,,,,,,,,,
+5,0.0,1.0,6.0,1.0,6.0,23.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.17211232558249998,0.08286397799189531,0.292599252995,0.05,,,,,,,,,,,,
+6,0.0,1.0,8.0,1.0,8.0,31.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.2198279677275562,0.11007233160822624,0.38627652786039945,0.05,,,,,,,,,,,,
+7,0.0,1.0,3.0,1.0,3.0,34.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+8,0.0,1.0,3.0,1.0,3.0,37.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+9,0.0,1.0,4.0,1.0,4.0,41.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+10,0.0,1.0,6.0,1.0,6.0,47.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.17211232558249998,0.08286397799189531,0.292599252995,0.05,,,,,,,,,,,,
+11,0.0,1.0,6.0,1.0,6.0,53.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.17211232558249998,0.08286397799189531,0.292599252995,0.05,,,,,,,,,,,,
+12,0.0,1.0,3.0,1.0,3.0,56.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+13,0.0,1.0,3.0,1.0,3.0,59.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+14,0.0,1.0,4.0,1.0,4.0,63.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+15,0.0,1.0,4.0,1.0,4.0,67.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+16,0.0,1.0,5.0,1.0,5.0,72.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1480149401,0.06896102301892251,0.2450497505,0.05,,,,,,,,,,,,
+17,0.0,1.0,10.0,1.0,10.0,82.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.2669127129358222,0.13661969731348275,0.4780896249559775,0.05,,,,,,,,,,,,
+18,0.0,1.0,3.0,1.0,3.0,85.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+19,0.0,1.0,6.0,1.0,6.0,91.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.17211232558249998,0.08286397799189531,0.292599252995,0.05,,,,,,,,,,,,
+20,0.0,1.0,4.0,1.0,4.0,95.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+21,0.0,1.0,5.0,1.0,5.0,100.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1480149401,0.06896102301892251,0.2450497505,0.05,,,,,,,,,,,,
+22,0.0,1.0,3.0,1.0,3.0,103.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+23,0.0,1.0,15.0,1.0,15.0,118.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.3819257031625212,0.20048895872248507,0.6997082267935573,0.05,,,,,,,,,,,,
+24,0.0,1.0,3.0,1.0,3.0,121.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+25,0.0,1.0,10.0,1.0,10.0,131.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.2669127129358222,0.13661969731348275,0.4780896249559775,0.05,,,,,,,,,,,,
+26,0.0,1.0,3.0,1.0,3.0,134.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,
+27,0.0,1.0,4.0,1.0,4.0,138.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+28,0.0,1.0,4.0,1.0,4.0,142.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+29,0.0,1.0,4.0,1.0,4.0,146.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+30,0.0,1.0,6.0,1.0,6.0,152.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.17211232558249998,0.08286397799189531,0.292599252995,0.05,,,,,,,,,,,,
+31,0.0,1.0,16.0,1.0,16.0,168.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.404474793247715,0.21286264494757515,0.7427111445256217,0.05,,,,,,,,,,,,
+32,0.0,1.0,4.0,1.0,4.0,172.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,
+33,0.0,1.0,6.0,1.0,6.0,178.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.17211232558249998,0.08286397799189531,0.292599252995,0.05,,,,,,,,,,,,
+34,0.0,1.0,9.0,1.0,9.0,187.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.2434486116002494,0.12342278391615898,0.4324137625817954,0.05,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_A3C_LSTM_hopper/trace.csv b/rl_coach/traces/Mujoco_A3C_LSTM_hopper/trace.csv
index a6514e9..e1e286b 100644
--- a/rl_coach/traces/Mujoco_A3C_LSTM_hopper/trace.csv
+++ b/rl_coach/traces/Mujoco_A3C_LSTM_hopper/trace.csv
@@ -1,261 +1,271 @@
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diff --git a/rl_coach/traces/Mujoco_A3C_LSTM_inverted_pendulum/trace.csv b/rl_coach/traces/Mujoco_A3C_LSTM_inverted_pendulum/trace.csv
index 7a2e250..4722da6 100644
--- a/rl_coach/traces/Mujoco_A3C_LSTM_inverted_pendulum/trace.csv
+++ b/rl_coach/traces/Mujoco_A3C_LSTM_inverted_pendulum/trace.csv
@@ -1,511 +1,566 @@
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+9,0.0,1.0,4.0,1.0,4.0,46.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,,,,,,,,,
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+21,0.0,1.0,7.0,1.0,7.0,129.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.19604960199429286,0.09655815980645327,0.33967326046505,0.05,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_A3C_hopper/trace.csv b/rl_coach/traces/Mujoco_A3C_hopper/trace.csv
index fc5581c..1838790 100644
--- a/rl_coach/traces/Mujoco_A3C_hopper/trace.csv
+++ b/rl_coach/traces/Mujoco_A3C_hopper/trace.csv
@@ -1,256 +1,256 @@
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+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
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+2,0.0,1.0,20.0,1.0,20.0,34.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,0.17600218405849968,0.17409081435461718,0.5197994032058006,-0.0013057342519407404,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_A3C_inverted_pendulum/trace.csv b/rl_coach/traces/Mujoco_A3C_inverted_pendulum/trace.csv
index c6462d4..4860a93 100644
--- a/rl_coach/traces/Mujoco_A3C_inverted_pendulum/trace.csv
+++ b/rl_coach/traces/Mujoco_A3C_inverted_pendulum/trace.csv
@@ -1,561 +1,536 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
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+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min
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+2,0.0,1.0,6.0,1.0,6.0,11.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.17211232558249998,0.08286397799189531,0.292599252995,0.05,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,4.0,1.0,4.0,15.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,14.0,1.0,14.0,29.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.35922695147459,0.1879852463102243,0.6562709361551083,0.05,,,,,,,,,,,,,,,,,,,,
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+6,0.0,1.0,7.0,1.0,7.0,41.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.19604960199429286,0.09655815980645327,0.33967326046505,0.05,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,11.0,1.0,11.0,52.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.29022144164224,0.14966996075313924,0.5233087287064176,0.05,,,,,,,,,,,,,,,,,,,,
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+12,0.0,1.0,3.0,1.0,3.0,84.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.099335,0.040214667100449804,0.148505,0.05,,,,,,,,,,,,,,,,,,,,
+13,0.0,1.0,10.0,1.0,10.0,94.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.2669127129358222,0.13661969731348275,0.4780896249559775,0.05,,,,,,,,,,,,,,,,,,,,
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+19,0.0,1.0,4.0,1.0,4.0,123.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1237562375,0.054791469323191344,0.19701995,0.05,,,,,,,,,,,,,,,,,,,,
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+21,0.0,1.0,7.0,1.0,7.0,142.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.19604960199429286,0.09655815980645327,0.33967326046505,0.05,,,,,,,,,,,,,,,,,,,,
+22,0.0,1.0,5.0,1.0,5.0,147.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.1480149401,0.06896102301892251,0.2450497505,0.05,,,,,,,,,,,,,,,,,,,,
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+31,0.0,1.0,8.0,1.0,8.0,199.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.2198279677275562,0.11007233160822624,0.38627652786039945,0.05,,,,,,,,,,,,,,,,,,,,
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+33,0.0,1.0,18.0,1.0,18.0,225.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.44912843987740936,0.23722760004486,0.8274311927495618,0.05,,,,,,,,,,,,,,,,,,,,
+34,0.0,1.0,13.0,1.0,13.0,238.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.3363774141914733,0.17534929825633458,0.6123948850051599,0.05,,,,,,,,,,,,,,,,,,,,
+35,0.0,1.0,13.0,1.0,13.0,251.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.3363774141914733,0.17534929825633458,0.6123948850051599,0.05,,,,,,,,,,,,,,,,,,,,
+36,0.0,1.0,9.0,1.0,9.0,260.0,0.1,,,0.0,,,,,,,,,,,,,,,,0.2434486116002494,0.12342278391615898,0.4324137625817954,0.05,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_ClippedPPO_hopper/trace.csv b/rl_coach/traces/Mujoco_ClippedPPO_hopper/trace.csv
index ebebb47..f07ca56 100644
--- a/rl_coach/traces/Mujoco_ClippedPPO_hopper/trace.csv
+++ b/rl_coach/traces/Mujoco_ClippedPPO_hopper/trace.csv
@@ -1,226 +1,226 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Value Targets/Mean,Value Targets/Stdev,Value Targets/Max,Value Targets/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min,Likelihood Ratio/Mean,Likelihood Ratio/Stdev,Likelihood Ratio/Max,Likelihood Ratio/Min,Clipped Likelihood Ratio/Mean,Clipped Likelihood Ratio/Stdev,Clipped Likelihood Ratio/Max,Clipped Likelihood Ratio/Min
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-216,2.0,0.0,1331.0,25.0,61.0,5437.0,0.10000000000000002,84.27060737248436,84.27060737248436,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-217,2.0,0.0,1381.0,26.0,50.0,5487.0,0.10000000000000002,63.28109318518307,63.28109318518307,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-218,2.0,0.0,1394.0,27.0,13.0,5500.0,0.10000000000000002,11.357912830361926,11.357912830361926,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-219,2.0,0.0,1415.0,28.0,21.0,5521.0,0.10000000000000002,23.843337169362858,23.843337169362858,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-220,2.0,0.0,1469.0,29.0,54.0,5575.0,0.10000000000000002,77.8349747169687,77.8349747169687,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-221,2.0,0.0,1577.0,30.0,108.0,5683.0,0.10000000000000002,193.14414742576,193.14414742576,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-222,2.0,0.0,1647.0,31.0,70.0,5753.0,0.10000000000000002,108.3755200909245,108.3755200909245,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-223,2.0,0.0,1672.0,32.0,25.0,5778.0,0.10000000000000002,24.275938630853904,24.275938630853904,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-224,2.0,0.0,1721.0,33.0,49.0,5827.0,0.10000000000000002,80.84369738981951,80.84369738981951,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-225,2.0,0.0,1758.0,34.0,37.0,5864.0,0.10000000000000002,34.52818930061657,34.52818930061657,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Value Targets/Mean,Value Targets/Stdev,Value Targets/Max,Value Targets/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min,Likelihood Ratio/Mean,Likelihood Ratio/Stdev,Likelihood Ratio/Max,Likelihood Ratio/Min,Clipped Likelihood Ratio/Mean,Clipped Likelihood Ratio/Stdev,Clipped Likelihood Ratio/Max,Clipped Likelihood Ratio/Min
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+2,0.0,1.0,40.0,2.0,23.0,40.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,6.444564121549556,4.523807302228816,14.892816778727136,0.4339427590058468,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,55.0,3.0,15.0,55.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,5.372082027351956,3.290894637318891,11.137801685828423,0.6193940407704618,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_ClippedPPO_inverted_pendulum/trace.csv b/rl_coach/traces/Mujoco_ClippedPPO_inverted_pendulum/trace.csv
index aea3672..78d772a 100644
--- a/rl_coach/traces/Mujoco_ClippedPPO_inverted_pendulum/trace.csv
+++ b/rl_coach/traces/Mujoco_ClippedPPO_inverted_pendulum/trace.csv
@@ -1,651 +1,636 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Value Targets/Mean,Value Targets/Stdev,Value Targets/Max,Value Targets/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min,Likelihood Ratio/Mean,Likelihood Ratio/Stdev,Likelihood Ratio/Max,Likelihood Ratio/Min,Clipped Likelihood Ratio/Mean,Clipped Likelihood Ratio/Stdev,Clipped Likelihood Ratio/Max,Clipped Likelihood Ratio/Min
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-628,2.0,0.0,1635.0,105.0,11.0,5740.0,0.1,11.0,11.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-629,2.0,0.0,1660.0,106.0,25.0,5765.0,0.1,25.0,25.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-630,2.0,0.0,1665.0,107.0,5.0,5770.0,0.1,5.0,5.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-631,2.0,0.0,1669.0,108.0,4.0,5774.0,0.1,4.0,4.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-632,2.0,0.0,1676.0,109.0,7.0,5781.0,0.1,7.0,7.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-633,2.0,0.0,1688.0,110.0,12.0,5793.0,0.1,12.0,12.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-637,2.0,0.0,1734.0,114.0,12.0,5839.0,0.1,12.0,12.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-638,2.0,0.0,1746.0,115.0,12.0,5851.0,0.1,12.0,12.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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-642,2.0,0.0,1803.0,119.0,19.0,5908.0,0.1,19.0,19.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-643,2.0,0.0,1819.0,120.0,16.0,5924.0,0.1,16.0,16.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-644,2.0,0.0,1831.0,121.0,12.0,5936.0,0.1,12.0,12.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-645,2.0,0.0,1839.0,122.0,8.0,5944.0,0.1,8.0,8.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-646,2.0,0.0,1848.0,123.0,9.0,5953.0,0.1,9.0,9.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-647,2.0,0.0,1861.0,124.0,13.0,5966.0,0.1,13.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-648,2.0,0.0,1883.0,125.0,22.0,5988.0,0.1,22.0,22.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-649,2.0,0.0,1896.0,126.0,13.0,6001.0,0.1,13.0,13.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-650,2.0,0.0,1901.0,127.0,5.0,6006.0,0.1,5.0,5.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Value Targets/Mean,Value Targets/Stdev,Value Targets/Max,Value Targets/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min,Likelihood Ratio/Mean,Likelihood Ratio/Stdev,Likelihood Ratio/Max,Likelihood Ratio/Min,Clipped Likelihood Ratio/Mean,Clipped Likelihood Ratio/Stdev,Clipped Likelihood Ratio/Max,Clipped Likelihood Ratio/Min
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+2,0.0,1.0,11.0,2.0,6.0,11.0,0.1,,,0.0,,,,,,,,,,,,,,,,3.44224651165,1.657279559837906,5.8519850599000005,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,15.0,3.0,4.0,15.0,0.1,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,27.0,4.0,12.0,27.0,0.1,,,0.0,,,,,,,,,,,,,,,,6.267519165806655,3.251571134644539,11.36151282838707,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+6,0.0,1.0,36.0,6.0,5.0,36.0,0.1,,,0.0,,,,,,,,,,,,,,,,2.960298802,1.3792204603784504,4.90099501,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,39.0,7.0,3.0,39.0,0.1,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,0.0,1.0,42.0,8.0,3.0,42.0,0.1,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,0.0,1.0,46.0,9.0,4.0,46.0,0.1,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+10,0.0,1.0,56.0,10.0,10.0,56.0,0.1,,,0.0,,,,,,,,,,,,,,,,5.338254258716446,2.7323939462696547,9.561792499119552,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+18,0.0,1.0,105.0,18.0,8.0,105.0,0.1,,,0.0,,,,,,,,,,,,,,,,4.396559354551124,2.2014466321645245,7.725530557207988,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+20,0.0,1.0,122.0,20.0,4.0,122.0,0.1,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+37,0.0,1.0,203.0,37.0,3.0,203.0,0.1,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+38,0.0,1.0,207.0,38.0,4.0,207.0,0.1,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+39,0.0,1.0,211.0,39.0,4.0,211.0,0.1,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+40,0.0,1.0,227.0,40.0,16.0,227.0,0.1,,,0.0,,,,,,,,,,,,,,,,8.089495864954301,4.257252898951503,14.85422289051243,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+41,0.0,1.0,233.0,41.0,6.0,233.0,0.1,,,0.0,,,,,,,,,,,,,,,,3.44224651165,1.657279559837906,5.8519850599000005,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+42,0.0,1.0,236.0,42.0,3.0,236.0,0.1,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+43,0.0,1.0,245.0,43.0,9.0,245.0,0.1,,,0.0,,,,,,,,,,,,,,,,4.868972232004988,2.4684556783231795,8.648275251635907,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_DDPG_hopper/trace.csv b/rl_coach/traces/Mujoco_DDPG_hopper/trace.csv
index bf4811c..4ad47a1 100644
--- a/rl_coach/traces/Mujoco_DDPG_hopper/trace.csv
+++ b/rl_coach/traces/Mujoco_DDPG_hopper/trace.csv
@@ -1,271 +1,271 @@
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+2,0.0,1.0,37.0,2.0,16.0,37.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.003888307749301,2.849757035873516,8.391397533459859,-0.2587612435243969,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,55.0,3.0,18.0,55.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.6786651393191905,2.709529829273535,8.321805201307981,-0.07569710079796449,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,74.0,4.0,19.0,74.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.090034225181489,3.206023887752558,10.509288520018028,0.2839392848619814,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,0.0,1.0,88.0,5.0,14.0,88.0,0.0,,,0.0,,,,,,,,,,,,,,,,7.165552231645576,3.66404512761854,13.023131858630245,1.0091819839290892,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,0.0,1.0,112.0,6.0,24.0,112.0,0.0,,,0.0,,,,,,,,,,,,,,,,10.731327473573936,6.126399979113884,20.66145903199017,0.6910095956009107,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,145.0,7.0,33.0,145.0,0.0,,,0.0,,,,,,,,,,,,,,,,10.670220549370404,6.992963119278488,22.791842039974323,0.6562351823457857,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,0.0,1.0,167.0,8.0,22.0,167.0,0.0,,,0.0,,,,,,,,,,,,,,,,7.034768970898434,4.2612714860199965,14.403915706302536,0.43413773599091454,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+10,0.0,1.0,200.0,10.0,22.0,200.0,0.0,,,0.0,,,,,,,,,,,,,,,,9.183232104480675,4.539275364816548,16.905503769460527,0.990589379968938,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+13,0.0,1.0,273.0,13.0,20.0,273.0,0.0,,,0.0,,,,,,,,,,,,,,,,8.265360232072668,4.473821209647469,15.469151456358226,0.8284490732033831,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+14,0.0,1.0,315.0,14.0,42.0,315.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.7169679168738905,4.6969997996313895,13.794534422051694,-2.4982641667979366,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,0.0,1.0,331.0,15.0,16.0,331.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.0898727857551376,2.9049257439094567,8.565105518472505,-0.05003811602960236,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+17,0.0,1.0,385.0,17.0,34.0,385.0,0.0,,,0.0,,,,,,,,,,,,,,,,20.957413390494548,10.410960745680466,35.285247617948336,1.3324838701860773,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,0.0,1.0,410.0,18.0,25.0,410.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.768713496015011,4.2546875470428835,13.23091237762408,-0.03728525471663438,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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+30,0.0,1.0,653.0,30.0,13.0,653.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.862142798232375,2.5984252326970823,9.374818345311445,0.7763175921468011,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,0.0,1.0,668.0,31.0,15.0,668.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.8194785964432527,3.2361657669765598,9.43777745336668,-0.12561880019859942,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+32,0.0,1.0,687.0,32.0,19.0,687.0,0.0,,,0.0,,,,,,,,,,,,,,,,10.858865353901095,5.467714525291249,19.06255729692779,1.1493968566031536,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+33,0.0,1.0,707.0,33.0,20.0,707.0,0.0,,,0.0,,,,,,,,,,,,,,,,6.683532552827747,4.140795929483716,14.121099278363204,0.7130054574672321,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+34,0.0,1.0,730.0,34.0,23.0,730.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.2268630582094662,2.241282259788747,6.722739252206602,-0.6901768563908945,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+35,0.0,1.0,748.0,35.0,18.0,748.0,0.0,,,0.0,,,,,,,,,,,,,,,,6.286256038027468,3.8973221567944663,13.20183733089751,0.503174338223436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+36,0.0,1.0,764.0,36.0,16.0,764.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.3573850442372417,2.8494186233879786,8.734287888427174,0.03318852071864545,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_DDPG_inverted_pendulum/trace.csv b/rl_coach/traces/Mujoco_DDPG_inverted_pendulum/trace.csv
index af7fdd6..ae9ff02 100644
--- a/rl_coach/traces/Mujoco_DDPG_inverted_pendulum/trace.csv
+++ b/rl_coach/traces/Mujoco_DDPG_inverted_pendulum/trace.csv
@@ -1,796 +1,801 @@
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+1,0.0,1.0,7.0,1.0,7.0,7.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.920992039885856,1.9311631961290656,6.793465209300998,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,0.0,1.0,16.0,2.0,9.0,16.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.868972232004988,2.4684556783231795,8.648275251635907,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,24.0,3.0,8.0,24.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.396559354551124,2.2014466321645245,7.725530557207988,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,32.0,4.0,8.0,32.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.396559354551124,2.2014466321645245,7.725530557207988,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,0.0,1.0,40.0,5.0,8.0,40.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.396559354551124,2.2014466321645245,7.725530557207988,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,0.0,1.0,45.0,6.0,5.0,45.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.960298802,1.3792204603784504,4.90099501,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,48.0,7.0,3.0,48.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,0.0,1.0,53.0,8.0,5.0,53.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.960298802,1.3792204603784504,4.90099501,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,0.0,1.0,57.0,9.0,4.0,57.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+10,0.0,1.0,62.0,10.0,5.0,62.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.960298802,1.3792204603784504,4.90099501,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,0.0,1.0,71.0,11.0,9.0,71.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.868972232004988,2.4684556783231795,8.648275251635907,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+12,0.0,1.0,74.0,12.0,3.0,74.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,0.0,1.0,78.0,13.0,4.0,78.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+14,0.0,1.0,81.0,14.0,3.0,81.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_NAF_hopper/trace.csv b/rl_coach/traces/Mujoco_NAF_hopper/trace.csv
index 1f95b81..abbc48f 100644
--- a/rl_coach/traces/Mujoco_NAF_hopper/trace.csv
+++ b/rl_coach/traces/Mujoco_NAF_hopper/trace.csv
@@ -1,131 +1,201 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,L/Mean,L/Stdev,L/Max,L/Min,Advantage/Mean,Advantage/Stdev,Advantage/Max,Advantage/Min,Action/Mean,Action/Stdev,Action/Max,Action/Min,V/Mean,V/Stdev,V/Max,V/Min,TD targets/Mean,TD targets/Stdev,TD targets/Max,TD targets/Min
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+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,L/Mean,L/Stdev,L/Max,L/Min,Advantage/Mean,Advantage/Stdev,Advantage/Max,Advantage/Min,Action/Mean,Action/Stdev,Action/Max,Action/Min,V/Mean,V/Stdev,V/Max,V/Min,TD targets/Mean,TD targets/Stdev,TD targets/Max,TD targets/Min
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+3,0.0,1.0,46.0,3.0,12.0,46.0,0.0,,,0.0,,,,,,,,,,,,,,,,5.593027234628188,2.964603800776864,10.304321656592007,0.8599701809506777,,,,,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,71.0,4.0,25.0,71.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.82852225200002,3.423968714645437,11.086182607801184,0.0063227053692522395,,,,,,,,,,,,,,,,,,,,,,,,
+5,0.0,1.0,103.0,5.0,32.0,103.0,0.0,,,0.0,,,,,,,,,,,,,,,,15.411674105404725,7.553428703437254,26.82464751899328,1.1057116167680792,,,,,,,,,,,,,,,,,,,,,,,,
+6,0.0,1.0,119.0,6.0,16.0,119.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.129185786167899,3.0770566350486543,8.79515484620401,-0.19986939457636774,,,,,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,147.0,7.0,28.0,147.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.030481874390181,3.3095648560141586,9.861815499813012,-0.9169861669991809,,,,,,,,,,,,,,,,,,,,,,,,
+8,0.0,1.0,204.0,8.0,57.0,204.0,0.0,,,0.0,,,,,,,,,,,,,,,,35.94155386453665,18.313663423557013,56.781451777257814,0.8334245595998659,,,,,,,,,,,,,,,,,,,,,,,,
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+11,0.0,1.0,255.0,11.0,25.0,255.0,0.0,,,0.0,,,,,,,,,,,,,,,,7.869881760816451,3.89902334860397,14.996012581179125,0.8241078274840928,,,,,,,,,,,,,,,,,,,,,,,,
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+16,0.0,1.0,371.0,16.0,43.0,371.0,0.0,,,0.0,,,,,,,,,,,,,,,,9.371537466083923,8.730238812317637,25.37775232492115,-0.06267820795931316,,,,,,,,,,,,,,,,,,,,,,,,
+17,0.0,1.0,390.0,17.0,19.0,390.0,0.0,,,0.0,,,,,,,,,,,,,,,,8.147071729082526,4.060754593498434,14.924827018854687,0.9164282652835236,,,,,,,,,,,,,,,,,,,,,,,,
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+19,0.0,1.0,429.0,19.0,15.0,429.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.738469728162474,2.5759429591120684,7.838772680566971,-0.00940036336216944,,,,,,,,,,,,,,,,,,,,,,,,
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+30,0.0,1.0,671.0,30.0,25.0,671.0,0.0,,,0.0,,,,,,,,,,,,,,,,5.020611793812134,4.057759540343849,13.0371638424986,0.04206686213911737,,,,,,,,,,,,,,,,,,,,,,,,
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+32,0.0,1.0,714.0,32.0,27.0,714.0,0.0,,,0.0,,,,,,,,,,,,,,,,9.07149958201815,5.4984796101031606,18.55815825395904,0.5294807809278668,,,,,,,,,,,,,,,,,,,,,,,,
+33,0.0,1.0,742.0,33.0,28.0,742.0,0.0,,,0.0,,,,,,,,,,,,,,,,11.061096389298642,5.8900781114439535,21.07911377452489,0.8491509366614628,,,,,,,,,,,,,,,,,,,,,,,,
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+35,0.0,1.0,788.0,35.0,18.0,788.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.640509558752013,3.4409062005447515,11.19697985877613,0.2390898613394384,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_NAF_inverted_pendulum/trace.csv b/rl_coach/traces/Mujoco_NAF_inverted_pendulum/trace.csv
index 96a61a7..521bd71 100644
--- a/rl_coach/traces/Mujoco_NAF_inverted_pendulum/trace.csv
+++ b/rl_coach/traces/Mujoco_NAF_inverted_pendulum/trace.csv
@@ -1,566 +1,841 @@
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+2,0.0,1.0,7.0,2.0,3.0,7.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,10.0,3.0,3.0,10.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,17.0,4.0,7.0,17.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.920992039885856,1.9311631961290656,6.793465209300998,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+5,0.0,1.0,23.0,5.0,6.0,23.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.44224651165,1.657279559837906,5.8519850599000005,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+6,0.0,1.0,31.0,6.0,8.0,31.0,0.0,,,0.0,,,,,,,,,,,,,,,,4.396559354551124,2.2014466321645245,7.725530557207988,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,34.0,7.0,3.0,34.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+8,0.0,1.0,37.0,8.0,3.0,37.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+9,0.0,1.0,41.0,9.0,4.0,41.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+10,0.0,1.0,47.0,10.0,6.0,47.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.44224651165,1.657279559837906,5.8519850599000005,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+11,0.0,1.0,53.0,11.0,6.0,53.0,0.0,,,0.0,,,,,,,,,,,,,,,,3.44224651165,1.657279559837906,5.8519850599000005,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+12,0.0,1.0,56.0,12.0,3.0,56.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+13,0.0,1.0,59.0,13.0,3.0,59.0,0.0,,,0.0,,,,,,,,,,,,,,,,1.9867,0.8042933420089962,2.9701,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+14,0.0,1.0,63.0,14.0,4.0,63.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+15,0.0,1.0,67.0,15.0,4.0,67.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.47512475,1.0958293864638269,3.940399,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+16,0.0,1.0,72.0,16.0,5.0,72.0,0.0,,,0.0,,,,,,,,,,,,,,,,2.960298802,1.3792204603784504,4.90099501,1.0,,,,,,,,,,,,,,,,,,,,,,,,
+17,0.0,1.0,82.0,17.0,10.0,82.0,0.0,,,0.0,,,,,,,,,,,,,,,,5.338254258716446,2.7323939462696547,9.561792499119552,1.0,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_PPO_hopper/trace.csv b/rl_coach/traces/Mujoco_PPO_hopper/trace.csv
index b5e83e4..44b4378 100644
--- a/rl_coach/traces/Mujoco_PPO_hopper/trace.csv
+++ b/rl_coach/traces/Mujoco_PPO_hopper/trace.csv
@@ -1,286 +1,286 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min
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+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min
+1,0.0,1.0,17.0,1.0,17.0,17.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,6.162379521079859,3.609954821692951,12.578831844357849,0.7107869962784428,,,,,,,,,,,,,,,,,,,,,,,,
+2,0.0,1.0,40.0,2.0,23.0,40.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,6.444564121549556,4.523807302228816,14.892816778727136,0.4339427590058468,,,,,,,,,,,,,,,,,,,,,,,,
+3,0.0,1.0,55.0,3.0,15.0,55.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,5.372082027351956,3.290894637318891,11.137801685828423,0.6193940407704618,,,,,,,,,,,,,,,,,,,,,,,,
+4,0.0,1.0,72.0,4.0,17.0,72.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,3.8701028966093562,3.270072541308888,9.923331866987294,0.005982662057427273,,,,,,,,,,,,,,,,,,,,,,,,
+5,0.0,1.0,81.0,5.0,9.0,81.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,3.224065401354734,1.8055626268612668,6.3106520357668385,0.6344330989380148,,,,,,,,,,,,,,,,,,,,,,,,
+6,0.0,1.0,102.0,6.0,21.0,102.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,4.120836876355532,3.8808284324851288,11.478788673172845,-0.3715598470211059,,,,,,,,,,,,,,,,,,,,,,,,
+7,0.0,1.0,124.0,7.0,22.0,124.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,13.836531534773533,6.565511751669497,23.01700211353013,1.4166070253466887,,,,,,,,,,,,,,,,,,,,,,,,
+8,0.0,1.0,138.0,8.0,14.0,138.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,6.952396701872661,3.6327187939027796,12.826708587894752,1.113121115744604,,,,,,,,,,,,,,,,,,,,,,,,
+9,0.0,1.0,157.0,9.0,19.0,157.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,9.717131847882532,4.611847906789345,16.938695655651763,1.2727482171414817,,,,,,,,,,,,,,,,,,,,,,,,
+10,0.0,1.0,188.0,10.0,31.0,188.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,16.67200187233022,8.315111028752034,29.28738542915201,1.1372862596442086,,,,,,,,,,,,,,,,,,,,,,,,
+11,0.0,1.0,201.0,11.0,13.0,201.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,4.932304963884506,2.852807494854594,9.932870348502739,0.7105412979992525,,,,,,,,,,,,,,,,,,,,,,,,
+12,0.0,1.0,212.0,12.0,11.0,212.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,3.3071493377403924,1.9818417655746363,6.91463554699037,0.4991920380165129,,,,,,,,,,,,,,,,,,,,,,,,
+13,0.0,1.0,245.0,13.0,33.0,245.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,5.471024214184932,4.893841216767363,15.615020321828332,-0.4030061981389668,,,,,,,,,,,,,,,,,,,,,,,,
+14,0.0,1.0,255.0,14.0,10.0,255.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,3.621864536400744,2.1833749132010034,7.219781637465878,0.4556503562617686,,,,,,,,,,,,,,,,,,,,,,,,
+15,0.0,1.0,280.0,15.0,25.0,280.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,14.859702363262382,7.789179754579218,26.299693392769807,1.2614517348930283,,,,,,,,,,,,,,,,,,,,,,,,
+16,0.0,1.0,306.0,16.0,26.0,306.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,4.404004293439066,3.211352122615288,11.202705458379226,0.19195651324650054,,,,,,,,,,,,,,,,,,,,,,,,
+17,0.0,1.0,317.0,17.0,11.0,317.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,4.571866858439874,2.2870126167114693,8.360574144601259,0.8061898878400585,,,,,,,,,,,,,,,,,,,,,,,,
+18,0.0,1.0,341.0,18.0,24.0,341.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,13.84651066272229,6.213451800705657,22.918050308226,1.3716574856717276,,,,,,,,,,,,,,,,,,,,,,,,
+19,0.0,1.0,352.0,19.0,11.0,352.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,2.759190164138209,2.2815568972095117,6.839215164754331,0.06450445852029114,,,,,,,,,,,,,,,,,,,,,,,,
+20,0.0,1.0,376.0,20.0,24.0,376.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,2.6687684917322927,2.7794351934579087,9.250942259373467,-0.026554964844324395,,,,,,,,,,,,,,,,,,,,,,,,
+21,0.0,1.0,391.0,21.0,15.0,391.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,6.2160730390210475,3.212816702632223,11.635159299241195,0.8919044549965414,,,,,,,,,,,,,,,,,,,,,,,,
+22,0.0,1.0,432.0,22.0,41.0,432.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,20.540626535772265,11.3207541298854,37.83364705963936,0.8861102971319834,,,,,,,,,,,,,,,,,,,,,,,,
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+27,0.0,1.0,563.0,27.0,18.0,563.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,5.313348771976883,3.5928402456486874,11.868451939437366,0.26821042429430203,,,,,,,,,,,,,,,,,,,,,,,,
+28,0.0,1.0,584.0,28.0,21.0,584.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,8.336675577218317,5.288637572894259,16.88358940196608,0.35328096351774824,,,,,,,,,,,,,,,,,,,,,,,,
+29,0.0,1.0,603.0,29.0,19.0,603.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,6.121338612356719,3.651769375522628,12.968766980708981,0.7133913489902243,,,,,,,,,,,,,,,,,,,,,,,,
+30,0.0,1.0,626.0,30.0,23.0,626.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,8.609032749795452,5.090066902698868,17.522818656682638,0.5784005691614696,,,,,,,,,,,,,,,,,,,,,,,,
+31,0.0,1.0,648.0,31.0,22.0,648.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,4.839301487109163,4.109784251201032,12.218591414423285,-0.2860665898517514,,,,,,,,,,,,,,,,,,,,,,,,
+32,0.0,1.0,661.0,32.0,13.0,661.0,0.10000000000000002,,,0.0,,,,,,,,,,,,,,,,6.461842054096423,3.3474236243110456,11.803215791322383,1.0322093784731534,,,,,,,,,,,,,,,,,,,,,,,,
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diff --git a/rl_coach/traces/Mujoco_PPO_inverted_pendulum/trace.csv b/rl_coach/traces/Mujoco_PPO_inverted_pendulum/trace.csv
index 81825e5..103c4e5 100644
--- a/rl_coach/traces/Mujoco_PPO_inverted_pendulum/trace.csv
+++ b/rl_coach/traces/Mujoco_PPO_inverted_pendulum/trace.csv
@@ -1,781 +1,781 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min
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+Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Discounted Return/Mean,Discounted Return/Stdev,Discounted Return/Max,Discounted Return/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,KL Divergence/Mean,KL Divergence/Stdev,KL Divergence/Max,KL Divergence/Min
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diff --git a/rl_coach/traces/Pendulum_HAC/trace.csv b/rl_coach/traces/Pendulum_HAC/trace.csv
index 64ca23b..7380eb9 100644
--- a/rl_coach/traces/Pendulum_HAC/trace.csv
+++ b/rl_coach/traces/Pendulum_HAC/trace.csv
@@ -1,6 +1,6 @@
-Episode #,Training Iter,In Heatup,ER #Transitions,ER #Episodes,Episode Length,Total steps,Epsilon,Shaped Training Reward,Training Reward,Update Target Network,Evaluation Reward,Shaped Evaluation Reward,Success Rate,Loss/Mean,Loss/Stdev,Loss/Max,Loss/Min,Learning Rate/Mean,Learning Rate/Stdev,Learning Rate/Max,Learning Rate/Min,Grads (unclipped)/Mean,Grads (unclipped)/Stdev,Grads (unclipped)/Max,Grads (unclipped)/Min,Entropy/Mean,Entropy/Stdev,Entropy/Max,Entropy/Min,Advantages/Mean,Advantages/Stdev,Advantages/Max,Advantages/Min,Values/Mean,Values/Stdev,Values/Max,Values/Min,Value Loss/Mean,Value Loss/Stdev,Value Loss/Max,Value Loss/Min,Policy Loss/Mean,Policy Loss/Stdev,Policy Loss/Max,Policy Loss/Min,Q/Mean,Q/Stdev,Q/Max,Q/Min,TD targets/Mean,TD targets/Stdev,TD targets/Max,TD targets/Min,actions/Mean,actions/Stdev,actions/Max,actions/Min
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