[MLA-1952] Add optional seed for gym action spaces (#5303) (#5315)

com.unity.ml-agents/CHANGELOG.md

 and this project adheres to
 [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
-
 ## [2.0.0-exp.1] - 2021-04-22
 ### Major Changes
 #### com.unity.ml-agents / com.unity.ml-agents.extensions (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
 - Fixed a bug where --results-dir has no effect. (#5269)
 - Fixed a bug where old `.pt` checkpoints were not deleted during training. (#5271)
+- The `UnityToGymWrapper` initializer now accepts an optional `action_space_seed` seed. If this is specified, it will
+be used to set the random seed on the resulting action space. (#5303)
+
 
 ## [1.9.1-preview] - 2021-04-13
 ### Major Changes

gym-unity/gym_unity/envs/__init__.py

 import itertools
 import numpy as np
-from typing import Any, Dict, List, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import gym
 from gym import error, spaces
         uint8_visual: bool = False,
         flatten_branched: bool = False,
         allow_multiple_obs: bool = False,
+        action_space_seed: Optional[int] = None,
     ):
         """
         Environment initialization
             containing the visual observations and the last element containing the array of vector observations.
             If False, returns a single np.ndarray containing either only a single visual observation or the array of
             vector observations.
+        :param action_space_seed: If non-None, will be used to set the random seed on created gym.Space instances.
         """
         self._env = unity_env
 
                 "The gym wrapper does not provide explicit support for both discrete "
                 "and continuous actions."
             )
+
+        if action_space_seed is not None:
+            self._action_space.seed(action_space_seed)
 
         # Set observations space
         list_spaces: List[gym.Space] = []
         return -float("inf"), float("inf")
 
     @property
-    def action_space(self):
+    def action_space(self) -> gym.Space:
         return self._action_space
 
     @property

gym-unity/gym_unity/tests/test_gym.py

         mock_env, mock_spec, mock_decision_step, mock_terminal_step
     )
     env = UnityToGymWrapper(mock_env)
-    assert isinstance(env, UnityToGymWrapper)
     assert isinstance(env.reset(), np.ndarray)
     actions = env.action_space.sample()
     assert actions.shape[0] == 2
     assert env.action_space.n == 5
 
 
+def test_action_space_seed():
+    mock_env = mock.MagicMock()
+    mock_spec = create_mock_group_spec()
+    mock_decision_step, mock_terminal_step = create_mock_vector_steps(mock_spec)
+    setup_mock_unityenvironment(
+        mock_env, mock_spec, mock_decision_step, mock_terminal_step
+    )
+    actions = []
+    for _ in range(0, 2):
+        env = UnityToGymWrapper(mock_env, action_space_seed=1337)
+        env.reset()
+        actions.append(env.action_space.sample())
+    assert (actions[0] == actions[1]).all()
+
+
 @pytest.mark.parametrize("use_uint8", [True, False], ids=["float", "uint8"])
 def test_gym_wrapper_visual(use_uint8):
     mock_env = mock.MagicMock()
 
     env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8)
     assert isinstance(env.observation_space, spaces.Box)
-    assert isinstance(env, UnityToGymWrapper)
     assert isinstance(env.reset(), np.ndarray)
     actions = env.action_space.sample()
     assert actions.shape[0] == 2
     )
 
     env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=True)
-    assert isinstance(env, UnityToGymWrapper)
     assert isinstance(env.observation_space, spaces.Tuple)
     assert len(env.observation_space) == 2
     reset_obs = env.reset()
 
     # check behavior for allow_multiple_obs = False
     env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=False)
-    assert isinstance(env, UnityToGymWrapper)
     assert isinstance(env.observation_space, spaces.Box)
     reset_obs = env.reset()
     assert isinstance(reset_obs, np.ndarray)
     )
 
     env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=True)
-    assert isinstance(env, UnityToGymWrapper)
     assert isinstance(env.observation_space, spaces.Tuple)
     assert len(env.observation_space) == 3
     reset_obs = env.reset()
 
     # check behavior for allow_multiple_obs = False
     env = UnityToGymWrapper(mock_env, uint8_visual=use_uint8, allow_multiple_obs=False)
-    assert isinstance(env, UnityToGymWrapper)
     assert isinstance(env.observation_space, spaces.Box)
     reset_obs = env.reset()
     assert isinstance(reset_obs, np.ndarray)