Added the first version of the GymWrapper

ml-agents-envs/setup.py

     packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
     zip_safe=False,
     install_requires=[
+        "gym",
         "cloudpickle",
         "grpcio>=1.11.0",
         "numpy>=1.14.1,<2.0",

ml-agents-envs/mlagents_envs/gym_wrapper.py

+from mlagents_envs.base_env import (
+    BaseEnv,
+    DecisionSteps,
+    TerminalSteps,
+    BehaviorSpec,
+    BehaviorName,
+    AgentId,
+    ActionType,
+)
+from mlagents_envs.exception import UnityActionException, UnityObservationException
+
+from typing import List, Tuple
+
+import numpy as np
+
+import gym
+
+
+class GymWrapper(BaseEnv):
+    _DEFAULT_BEHAVIOR_NAME = "gym_behavior_name"
+    _AGENT_ID = 1
+
+    def __init__(self, gym_env, name=None):
+        self._gym_env = gym_env
+        self._first_message = True
+        self._behavior_name = name
+        if self._behavior_name is None:
+            self._behavior_name = self._DEFAULT_BEHAVIOR_NAME
+        action_type = ActionType.CONTINUOUS
+        action_shape = 0
+        if isinstance(self._gym_env.action_space, gym.spaces.Box):
+            action_type = ActionType.CONTINUOUS
+            action_shape = np.prod(self._gym_env.action_space.shape)
+        elif isinstance(self._gym_env.action_space, gym.spaces.Discrete):
+            action_shape = (self._gym_env.action_space.n,)
+            action_type = ActionType.DISCRETE
+        else:
+            raise UnityActionException(
+                f"Unknown action type {self._gym_env.action_space}"
+            )
+        self.obs_ratio = np.maximum(
+            self._gym_env.observation_space.high, -self._gym_env.observation_space.low
+        )
+        if not isinstance(self._gym_env.observation_space, gym.spaces.Box):
+            raise UnityObservationException(
+                f"Unknown observation type {self._gym_env.observation_space}"
+            )
+        self._behavior_specs = BehaviorSpec(
+            observation_shapes=[self._gym_env.observation_space.shape],
+            action_type=action_type,
+            action_shape=action_shape,
+        )
+        self._g_action: np.ndarray = None
+        self._current_steps: Tuple[DecisionSteps, TerminalSteps] = (None, None)
+        print(self._gym_env._max_episode_steps)
+
+    def step(self) -> None:
+        if self._first_message:
+            self.reset()
+            return
+        obs, rew, don, info = self._gym_env.step(self._g_action)
+        if not don:
+            self._current_steps = (
+                DecisionSteps(
+                    obs=[np.expand_dims(obs / self.obs_ratio, axis=0)],
+                    reward=np.array([rew]),
+                    agent_id=np.array([self._AGENT_ID]),
+                    action_mask=None,
+                ),
+                TerminalSteps.empty(self._behavior_specs),
+            )
+        else:
+            self._first_message = True
+            self._current_steps = (
+                DecisionSteps.empty(self._behavior_specs),
+                TerminalSteps(
+                    obs=[np.expand_dims(obs / self.obs_ratio, axis=0)],
+                    reward=np.array([rew]),
+                    max_step=np.array([info.get("TimeLimit.truncated", False)]),
+                    agent_id=np.array([self._AGENT_ID]),
+                ),
+            )
+
+    def reset(self) -> None:
+        self._first_message = False
+        obs = self._gym_env.reset()
+        self._current_steps = (
+            DecisionSteps(
+                obs=[np.expand_dims(obs / self.obs_ratio, axis=0)],
+                reward=np.array([0]),
+                agent_id=np.array([self._AGENT_ID]),
+                action_mask=None,
+            ),
+            TerminalSteps.empty(self._behavior_specs),
+        )
+
+    def close(self) -> None:
+        self._gym_env.close()
+
+    def get_behavior_names(self) -> List[BehaviorName]:
+        return [self._behavior_name]
+
+    def set_actions(self, behavior_name: BehaviorName, action: np.ndarray) -> None:
+        assert behavior_name == self._behavior_name
+        spec = self._behavior_specs
+        expected_type = np.float32 if spec.is_action_continuous() else np.int32
+        expected_shape = (1, spec.action_size)
+        if action.shape != expected_shape:
+            raise UnityActionException(
+                "The behavior {0} needs an input of dimension {1} but received input of dimension {2}".format(
+                    behavior_name, expected_shape, action.shape
+                )
+            )
+        if action.dtype != expected_type:
+            action = action.astype(expected_type)
+        if isinstance(self._gym_env.action_space, gym.spaces.Discrete):
+            self._g_action = int(action[0, 0])
+        elif isinstance(self._gym_env.action_space, gym.spaces.Box):
+            self._g_action = action[0]
+        else:
+            raise UnityActionException(
+                f"Unknown action type {self._gym_env.action_space}"
+            )
+
+    def set_action_for_agent(
+        self, behavior_name: BehaviorName, agent_id: AgentId, action: np.ndarray
+    ) -> None:
+        assert behavior_name == self._behavior_name
+        assert agent_id == self._AGENT_ID
+        spec = self._behavior_specs
+        expected_shape = (spec.action_size,)
+        if action.shape != expected_shape:
+            raise UnityActionException(
+                f"The Agent {0} with BehaviorName {1} needs an input of dimension "
+                f"{2} but received input of dimension {3}".format(
+                    agent_id, behavior_name, expected_shape, action.shape
+                )
+            )
+        expected_type = np.float32 if spec.is_action_continuous() else np.int32
+        if action.dtype != expected_type:
+            action = action.astype(expected_type)
+        if isinstance(self._gym_env.action_space, gym.spaces.Discrete):
+            self._g_action = int(action[0])
+        elif isinstance(self._gym_env.action_space, gym.spaces.Box):
+            self._g_action = action
+        else:
+            raise UnityActionException(
+                f"Unknown action type {self._gym_env.action_space}"
+            )
+
+    def get_steps(
+        self, behavior_name: BehaviorName
+    ) -> Tuple[DecisionSteps, TerminalSteps]:
+        assert behavior_name == self._behavior_name
+        return self._current_steps
+
+    def get_behavior_spec(self, behavior_name: BehaviorName) -> BehaviorSpec:
+        assert behavior_name == self._behavior_name
+        return self._behavior_specs