Robosuite exploration (#478)

* Add Robosuite parameters for all env types + initialize env flow

* Init flow done

* Rest of Environment API complete for RobosuiteEnvironment

* RobosuiteEnvironment changes

* Observation stacking filter
* Add proper frame_skip in addition to control_freq
* Hardcode Coach rendering to 'frontview' camera

* Robosuite_Lift_DDPG preset + Robosuite env updates

* Move observation stacking filter from env to preset
* Pre-process observation - concatenate depth map (if exists)
  to image and object state (if exists) to robot state
* Preset parameters based on Surreal DDPG parameters, taken from:
  https://github.com/SurrealAI/surreal/blob/master/surreal/main/ddpg_configs.py

* RobosuiteEnvironment fixes - working now with PyGame rendering

* Preset minor modifications

* ObservationStackingFilter - option to concat non-vector observations

* Consider frame skip when setting horizon in robosuite env

* Robosuite lift preset - update heatup length and training interval

* Robosuite env - change control_freq to 10 to match Surreal usage

* Robosuite clipped PPO preset

* Distribute multiple workers (-n #) over multiple GPUs

* Clipped PPO memory optimization from @shadiendrawis

* Fixes to evaluation only workers

* RoboSuite_ClippedPPO: Update training interval

* Undo last commit (update training interval)

* Fix "doube-negative" if conditions

* multi-agent single-trainer clipped ppo training with cartpole

* cleanups (not done yet) + ~tuned hyper-params for mast

* Switch to Robosuite v1 APIs

* Change presets to IK controller

* more cleanups + enabling evaluation worker + better logging

* RoboSuite_Lift_ClippedPPO updates

* Fix major bug in obs normalization filter setup

* Reduce coupling between Robosuite API and Coach environment

* Now only non task-specific parameters are explicitly defined
  in Coach
* Removed a bunch of enums of Robosuite elements, using simple
  strings instead
* With this change new environments/robots/controllers in Robosuite
  can be used immediately in Coach

* MAST: better logging of actor-trainer interaction + bug fixes + performance improvements.

Still missing: fixed pubsub for obs normalization running stats + logging for trainer signals

* lstm support for ppo

* setting JOINT VELOCITY action space by default + fix for EveryNEpisodes video dump filter + new TaskIDDumpFilter + allowing or between video dump filters

* Separate Robosuite clipped PPO preset for the non-MAST case

* Add flatten layer to architectures and use it in Robosuite presets

This is required for embedders that mix conv and dense

TODO: Add MXNet implementation

* publishing running_stats together with the published policy + hyper-param for when to publish a policy + cleanups

* bug-fix for memory leak in MAST

* Bugfix: Return value in TF BatchnormActivationDropout.to_tf_instance

* Explicit activations in embedder scheme so there's no ReLU after flatten

* Add clipped PPO heads with configurable dense layers at the beginning

* This is a workaround needed to mimic Surreal-PPO, where the CNN and
  LSTM are shared between actor and critic but the FC layers are not
  shared
* Added a "SchemeBuilder" class, currently only used for the new heads
  but we can change Middleware and Embedder implementations to use it
  as well

* Video dump setting fix in basic preset

* logging screen output to file

* coach to start the redis-server for a MAST run

* trainer drops off-policy data + old policy in ClippedPPO updates only after policy was published + logging free memory stats + actors check for a new policy only at the beginning of a new episode + fixed a bug where the trainer was logging "Training Reward = 0", causing dashboard to incorrectly display the signal

* Add missing set_internal_state function in TFSharedRunningStats

* Robosuite preset - use SingleLevelSelect instead of hard-coded level

* policy ID published directly on Redis

* Small fix when writing to log file

* Major bugfix in Robosuite presets - pass dense sizes to heads

* RoboSuite_Lift_ClippedPPO hyper-params update

* add horizon and value bootstrap to GAE calculation, fix A3C with LSTM

* adam hyper-params from mujoco

* updated MAST preset with IK_POSE_POS controller

* configurable initialization for policy stdev + custom extra noise per actor + logging of policy stdev to dashboard

* values loss weighting of 0.5

* minor fixes + presets

* bug-fix for MAST  where the old policy in the trainer had kept updating every training iter while it should only update after every policy publish

* bug-fix: reset_internal_state was not called by the trainer

* bug-fixes in the lstm flow + some hyper-param adjustments for CartPole_ClippedPPO_LSTM -> training and sometimes reaches 200

* adding back the horizon hyper-param - a messy commit

* another bug-fix missing from prev commit

* set control_freq=2 to match action_scale 0.125

* ClippedPPO with MAST cleanups and some preps for TD3 with MAST

* TD3 presets. RoboSuite_Lift_TD3 seems to work well with multi-process runs (-n 8)

* setting termination on collision to be on by default

* bug-fix following prev-prev commit

* initial cube exploration environment with TD3 commit

* bug fix + minor refactoring

* several parameter changes and RND debugging

* Robosuite Gym wrapper + Rename TD3_Random* -> Random*

* algorithm update

* Add RoboSuite v1 env + presets (to eventually replace non-v1 ones)

* Remove grasping presets, keep only V1 exp. presets (w/o V1 tag)

* Keep just robosuite V1 env as the 'robosuite_environment' module

* Exclude Robosuite and MAST presets from integration tests

* Exclude LSTM and MAST presets from golden tests

* Fix mistakenly removed import

* Revert debug changes in ReaderWriterLock

* Try another way to exclude LSTM/MAST golden tests

* Remove debug prints

* Remove PreDense heads, unused in the end

* Missed removing an instance of PreDense head

* Remove MAST, not required for this PR

* Undo unused concat option in ObservationStackingFilter

* Remove LSTM updates, not required in this PR

* Update README.md

* code changes for the exploration flow to work with robosuite master branch

* code cleanup + documentation

* jupyter tutorial for the goal-based exploration + scatter plot

* typo fix

* Update README.md

* seprate parameter for the obs-goal observation + small fixes

* code clarity fixes

* adjustment in tutorial 5

* Update tutorial

* Update tutorial

Co-authored-by: Guy Jacob <guy.jacob@intel.com>
Co-authored-by: Gal Leibovich <gal.leibovich@intel.com>
Co-authored-by: shadi.endrawis <sendrawi@aipg-ra-skx-03.ra.intel.com>

README.md

@@ -45,6 +45,7 @@ coach -p CartPole_DQN -r
   * [Distributed Multi-Node Coach](#distributed-multi-node-coach)
   * [Batch Reinforcement Learning](#batch-reinforcement-learning)
 - [Supported Environments](#supported-environments)
+  * [Note on MuJoCo version](#note-on-mujoco-version)
 - [Supported Algorithms](#supported-algorithms)
 - [Citation](#citation)
 - [Contact](#contact)
@@ -202,7 +203,7 @@ There are [example](https://github.com/IntelLabs/coach/blob/master/rl_coach/pres
 
 * *OpenAI Gym:*
 
-    Installed by default by Coach's installer
+    Installed by default by Coach's installer (see note on MuJoCo version [below](#note-on-mujoco-version)).
 
 * *ViZDoom:*
 
@@ -258,6 +259,18 @@ There are [example](https://github.com/IntelLabs/coach/blob/master/rl_coach/pres
     
     https://github.com/deepmind/dm_control
 
+* *Robosuite:*<a name="robosuite"></a>
+
+    **__Note:__ To use Robosuite-based environments, please install Coach from the latest cloned repository. It is not yet available as part of the `rl_coach` package on PyPI.**
+    
+    Follow the instructions described in the [robosuite documentation](https://robosuite.ai/docs/installation.html) (see note on MuJoCo version [below](#note-on-mujoco-version)).
+
+### Note on MuJoCo version
+
+OpenAI Gym supports MuJoCo only up to version 1.5 (and corresponding mujoco-py version 1.50.x.x). The Robosuite simulation framework, however, requires MuJoCo version 2.0 (and corresponding mujoco-py version 2.0.2.9, as of robosuite version 1.2). Therefore, if you wish to run both Gym-based MuJoCo environments and Robosuite environments, it's recommended to have a separate virtual environment for each.
+
+Please note that all Gym-Based MuJoCo presets in Coach (`rl_coach/presets/Mujoco_*.py`) have been validated _**only**_ with MuJoCo 1.5 (including the reported [benchmark results](benchmarks)).
+    
 
 ## Supported Algorithms