Create a dataset using an agent (#306)

Generate a dataset using an agent (allowing to select between this and a random dataset)

rl_coach/tests/exploration_policies/test_additive_noise.py

@@ -16,10 +16,6 @@ def test_init():
     action_space = DiscreteActionSpace(3)
     noise_schedule = LinearSchedule(1.0, 1.0, 1000)
 
-    # additive noise doesn't work for discrete controls
-    with pytest.raises(ValueError):
-        policy = AdditiveNoise(action_space, noise_schedule, 0)
-
     # additive noise requires a bounded range for the actions
     action_space = BoxActionSpace(np.array([10]))
     with pytest.raises(ValueError):

rl_coach/tests/exploration_policies/test_e_greedy.py

@@ -21,14 +21,14 @@ def test_get_action():
     # verify that test phase gives greedy actions (evaluation_epsilon = 0)
     policy.change_phase(RunPhase.TEST)
     for i in range(100):
-        best_action = policy.get_action(np.array([10, 20, 30]))
+        best_action, _ = policy.get_action(np.array([10, 20, 30]))
         assert best_action == 2
 
     # verify that train phase gives uniform actions (exploration = 1)
     policy.change_phase(RunPhase.TRAIN)
     counters = np.array([0, 0, 0])
     for i in range(30000):
-        best_action = policy.get_action(np.array([10, 20, 30]))
+        best_action, _ = policy.get_action(np.array([10, 20, 30]))
         counters[best_action] += 1
     assert np.all(counters > 9500)  # this is noisy so we allow 5% error
 

rl_coach/tests/exploration_policies/test_greedy.py

@@ -15,7 +15,7 @@ def test_get_action():
     action_space = DiscreteActionSpace(3)
     policy = Greedy(action_space)
 
-    best_action = policy.get_action(np.array([10, 20, 30]))
+    best_action, _ = policy.get_action(np.array([10, 20, 30]))
     assert best_action == 2
 
     # continuous control

rl_coach/tests/exploration_policies/test_ou_process.py

@@ -16,10 +16,6 @@ def test_init():
     # discrete control
     action_space = DiscreteActionSpace(3)
 
-    # OU process doesn't work for discrete controls
-    with pytest.raises(ValueError):
-        policy = OUProcess(action_space, mu=0, theta=0.1, sigma=0.2, dt=0.01)
-
 
 @pytest.mark.unit_test
 def test_get_action():