pre-release 0.10.0

rl_coach/tests/exploration_policies/test_additive_noise.py

@@ -0,0 +1,44 @@
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
+
+import pytest
+
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
+from rl_coach.exploration_policies.additive_noise import AdditiveNoise
+from rl_coach.schedules import LinearSchedule
+import numpy as np
+
+
+@pytest.mark.unit_test
+def test_init():
+    # discrete control
+    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):
+        policy = AdditiveNoise(action_space, noise_schedule, 0)
+
+
+@pytest.mark.unit_test
+def test_get_action():
+    # make sure noise is in range
+    action_space = BoxActionSpace(np.array([10]), -1, 1)
+    noise_schedule = LinearSchedule(1.0, 1.0, 1000)
+    policy = AdditiveNoise(action_space, noise_schedule, 0)
+
+    # the action range is 2, so there is a ~0.1% chance that the noise will be larger than 3*std=3*2=6
+    for i in range(1000):
+        action = policy.get_action(np.zeros([10]))
+        assert np.all(action < 10)
+        # make sure there is no clipping of the action since it should be the environment that clips actions
+        assert np.all(action != 1.0)
+        assert np.all(action != -1.0)
+        # make sure that each action element has a different value
+        assert np.all(action[0] != action[1:])