Support gymnasium environment

nnabla_rl/environments/__init__.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021,2022,2023 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,14 +14,16 @@
 # limitations under the License.
 
 from gym.envs.registration import register
+from gymnasium.envs.registration import register as gymnasium_register
 
 from nnabla_rl.environments.dummy import (DummyAtariEnv, DummyContinuous, DummyContinuousActionGoalEnv, DummyDiscrete,  # noqa
                                           DummyDiscreteActionGoalEnv, DummyDiscreteImg, DummyContinuousImg,
                                           DummyFactoredContinuous, DummyMujocoEnv,
                                           DummyTupleContinuous, DummyTupleDiscrete, DummyTupleMixed,
                                           DummyTupleStateContinuous, DummyTupleStateDiscrete,
                                           DummyTupleActionContinuous, DummyTupleActionDiscrete,
-                                          DummyHybridEnv)
+                                          DummyHybridEnv,
+                                          DummyGymnasiumAtariEnv, DummyGymnasiumMujocoEnv)
 
 register(
     id='FakeMujocoNNablaRL-v1',
@@ -87,3 +89,16 @@
     entry_point='nnabla_rl.environments.dummy:DummyHybridEnv',
     max_episode_steps=10
 )
+
+
+gymnasium_register(
+    id='FakeGymnasiumMujocoNNablaRL-v1',
+    entry_point='nnabla_rl.environments.dummy:DummyGymnasiumMujocoEnv',
+    max_episode_steps=10
+)
+
+gymnasium_register(
+    id='FakeGymnasiumAtariNNablaRLNoFrameskip-v1',
+    entry_point='nnabla_rl.environments.dummy:DummyGymnasiumAtariEnv',
+    max_episode_steps=10
+)

nnabla_rl/environments/dummy.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021,2022,2023 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,8 +16,10 @@
 from typing import TYPE_CHECKING, cast
 
 import gym
+import gymnasium
 import numpy as np
 from gym.envs.registration import EnvSpec
+from gymnasium.envs.registration import EnvSpec as GymnasiumEnvSpec
 
 if TYPE_CHECKING:
     from gym.utils.seeding import RandomNumberGenerator
@@ -309,3 +311,88 @@ def __init__(self, max_episode_steps=None):
         super(DummyHybridEnv, self).__init__(max_episode_steps=max_episode_steps)
         self.action_space = gym.spaces.Tuple((gym.spaces.Discrete(5), gym.spaces.Box(low=0.0, high=1.0, shape=(5, ))))
         self.observation_space = gym.spaces.Box(low=0.0, high=1.0, shape=(5, ))
+
+
+# =========== gymnasium ==========
+class AbstractDummyGymnasiumEnv(gymnasium.Env):
+    def __init__(self, max_episode_steps):
+        self.spec = GymnasiumEnvSpec('dummy-v0', max_episode_steps=max_episode_steps)
+        self._episode_steps = 0
+
+    def reset(self):
+        self._episode_steps = 0
+        return self.observation_space.sample(), {}
+
+    def step(self, a):
+        next_state = self.observation_space.sample()
+        reward = np.random.randn()
+        terminated = False
+        if self.spec.max_episode_steps is None:
+            truncated = False
+        else:
+            truncated = bool(self._episode_steps < self.spec.max_episode_steps)
+        info = {'rnn_states': {'dummy_scope': {'dummy_state1': 1, 'dummy_state2': 2}}}
+        self._episode_steps += 1
+        return next_state, reward, terminated, truncated, info
+
+
+class DummyGymnasiumAtariEnv(AbstractDummyGymnasiumEnv):
+    class DummyALE(object):
+        def __init__(self):
+            self._lives = 100
+
+        def lives(self):
+            self._lives -= 1
+            if self._lives < 0:
+                self._lives = 100
+            return self._lives
+
+    # seeding.np_random outputs np_random and seed
+    np_random = cast("RandomNumberGenerator", nnabla_rl.random.drng)
+
+    def __init__(self, done_at_random=True, max_episode_length=None):
+        super(DummyGymnasiumAtariEnv, self).__init__(
+            max_episode_steps=max_episode_length)
+        self.action_space = gymnasium.spaces.Discrete(4)
+        self.observation_space = gymnasium.spaces.Box(
+            low=0, high=255, shape=(84, 84, 3), dtype=np.uint8)
+        self.ale = DummyGymnasiumAtariEnv.DummyALE()
+        self._done_at_random = done_at_random
+        self._max_episode_length = max_episode_length
+        self._episode_length = None
+
+    def step(self, action):
+        assert self._episode_length is not None
+        observation = self.observation_space.sample()
+        self._episode_length += 1
+        if self._done_at_random:
+            done = bool(self.np_random.integers(10) == 0)
+        else:
+            done = False
+        if self._max_episode_length is not None:
+            done = (self._max_episode_length <= self._episode_length) or done
+        return observation, 1.0, done, {'needs_reset': False}
+
+    def reset(self):
+        self._episode_length = 0
+        return self.observation_space.sample()
+
+    def get_action_meanings(self):
+        return ['NOOP', 'FIRE', 'LEFT', 'RIGHT']
+
+
+class DummyGymnasiumMujocoEnv(AbstractDummyGymnasiumEnv):
+    def __init__(self, max_episode_steps=None):
+        super(DummyGymnasiumMujocoEnv, self).__init__(max_episode_steps=max_episode_steps)
+        self.action_space = gymnasium.spaces.Box(low=0.0, high=1.0, shape=(5, ))
+        self.observation_space = gymnasium.spaces.Box(low=0.0, high=1.0, shape=(5, ))
+
+    def get_dataset(self):
+        dataset = {}
+        datasize = 2000
+        dataset['observations'] = np.stack([self.observation_space.sample() for _ in range(datasize)], axis=0)
+        dataset['actions'] = np.stack([self.action_space.sample() for _ in range(datasize)], axis=0)
+        dataset['rewards'] = np.random.randn(datasize, 1)
+        dataset['terminals'] = np.random.randint(2, size=(datasize, 1))
+        dataset['timeouts'] = np.zeros((datasize, 1))
+        return dataset

nnabla_rl/environments/wrappers/__init__.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021,2022,2023 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -20,3 +20,4 @@
 from nnabla_rl.environments.wrappers.atari import make_atari, wrap_deepmind  # noqa
 from nnabla_rl.environments.wrappers.hybrid_env import (EmbedActionWrapper, FlattenActionWrapper,  # noqa
                                                         RemoveStepWrapper, ScaleActionWrapper, ScaleStateWrapper)
+from nnabla_rl.environments.wrappers.gymnasium import Gymnasium2GymWrapper  # noqa

nnabla_rl/environments/wrappers/atari.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021,2022,2023 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,10 +16,12 @@
 
 import cv2
 import gym
+import gymnasium
 import numpy as np
 from gym import spaces
 
 import nnabla_rl as rl
+from nnabla_rl.environments.wrappers.gymnasium import Gymnasium2GymWrapper
 from nnabla_rl.external.atari_wrappers import (ClipRewardEnv, EpisodicLifeEnv, FireResetEnv, MaxAndSkipEnv,
                                                NoopResetEnv, ScaledFloatFrame)
 
@@ -97,8 +99,14 @@ def __array__(self, dtype=None):
         return out
 
 
-def make_atari(env_id, max_frames_per_episode=None):
-    env = gym.make(env_id)
+def make_atari(env_id, max_frames_per_episode=None, use_gymnasium=False):
+    if use_gymnasium:
+        env = gymnasium.make(env_id)
+        env = Gymnasium2GymWrapper(env)
+        # gymnasium env is not wrapped TimeLimit wrapper
+        env = gym.wrappers.TimeLimit(env, max_episode_steps=env.spec.kwargs["max_num_frames_per_episode"])
+    else:
+        env = gym.make(env_id)
     if max_frames_per_episode is not None:
         env = env.unwrapped
         env = gym.wrappers.TimeLimit(env, max_episode_steps=max_frames_per_episode)

nnabla_rl/environments/wrappers/gymnasium.py

@@ -0,0 +1,88 @@
+# Copyright 2024 Sony Group Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional
+
+import gym
+from gym import spaces as gym_spaces
+from gymnasium import spaces as gymnasium_spaces
+from gymnasium.utils import seeding
+
+
+class Gymnasium2GymWrapper(gym.Wrapper):
+    def __init__(self, env):
+        if isinstance(env, gym.Env) or isinstance(env, gym.Wrapper):
+            raise ValueError("'env' should not be an instance of 'gym.Env' and 'gym.Wrapper'")
+
+        super().__init__(env)
+
+        # observation space
+        if isinstance(env.observation_space, gymnasium_spaces.Tuple):
+            self.observation_space = gym_spaces.Tuple(
+                [self._translate_space(observation_space)
+                 for observation_space in env.observation_space]
+            )
+        elif isinstance(env.observation_space, gymnasium_spaces.Dict):
+            self.observation_space = gym_spaces.Dict(
+                {key: self._translate_space(observation_space)
+                 for key, observation_space in env.observation_space.items()}
+            )
+        else:
+            self.observation_space = self._translate_space(env.observation_space)
+
+        # action space
+        if isinstance(env.action_space, gymnasium_spaces.Tuple):
+            self.action_space = gym_spaces.Tuple(
+                [self._translate_space(action_space)
+                 for action_space in env.action_space]
+            )
+        elif isinstance(env.action_space, gymnasium_spaces.Dict):
+            self.action_space = gym_spaces.Dict(
+                {key: self._translate_space(action_space)
+                 for key, action_space in env.action_space.items()}
+            )
+        else:
+            self.action_space = self._translate_space(env.action_space)
+
+    def reset(self):
+        obs, _ = self.env.reset()
+        return obs
+
+    def step(self, action):
+        obs, reward, terminated, truncated, info = self.env.step(action)
+        done = (terminated or truncated)
+        info.update({"TimeLimit.truncated": truncated})
+        return obs, reward, done, info
+
+    def seed(self, seed: Optional[int] = None):
+        np_random, seed = seeding.np_random(seed)
+        self.env.np_random = np_random  # type: ignore
+        return [seed]
+
+    @property
+    def unwrapped(self):
+        return self
+
+    def _translate_space(self, space):
+        if isinstance(space, gymnasium_spaces.Box):
+            return gym_spaces.Box(
+                low=space.low,
+                high=space.high,
+                shape=space.shape,
+                dtype=space.dtype
+            )
+        elif isinstance(space, gymnasium_spaces.Discrete):
+            return gym_spaces.Discrete(n=int(space.n))
+        else:
+            raise NotImplementedError

nnabla_rl/utils/reproductions.py

@@ -1,5 +1,5 @@
 # Copyright 2020,2021 Sony Corporation.
-# Copyright 2021,2022 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,12 +16,14 @@
 import random as py_random
 
 import gym
+import gymnasium
 import numpy as np
 
 import nnabla as nn
 import nnabla_rl as rl
 from nnabla_rl.environments.environment_info import EnvironmentInfo
-from nnabla_rl.environments.wrappers import NumpyFloat32Env, ScreenRenderEnv, make_atari, wrap_deepmind
+from nnabla_rl.environments.wrappers import (Gymnasium2GymWrapper, NumpyFloat32Env, ScreenRenderEnv, make_atari,
+                                             wrap_deepmind)
 from nnabla_rl.logger import logger
 
 
@@ -53,11 +55,14 @@ def build_atari_env(id_or_env,
                     print_info=True,
                     max_frames_per_episode=None,
                     frame_stack=True,
-                    flicker_probability=0.0):
+                    flicker_probability=0.0,
+                    use_gymnasium=False):
     if isinstance(id_or_env, gym.Env):
         env = id_or_env
+    elif isinstance(id_or_env, gymnasium.Env):
+        env = Gymnasium2GymWrapper(id_or_env)
     else:
-        env = make_atari(id_or_env, max_frames_per_episode=max_frames_per_episode)
+        env = make_atari(id_or_env, max_frames_per_episode=max_frames_per_episode, use_gymnasium=use_gymnasium)
     if print_info:
         print_env_info(env)
 
@@ -75,7 +80,7 @@ def build_atari_env(id_or_env,
     return env
 
 
-def build_mujoco_env(id_or_env, test=False, seed=None, render=False, print_info=True):
+def build_mujoco_env(id_or_env, test=False, seed=None, render=False, print_info=True, use_gymnasium=False):
     try:
         # Add pybullet env
         import pybullet_envs  # noqa
@@ -91,8 +96,14 @@ def build_mujoco_env(id_or_env, test=False, seed=None, render=False, print_info=
 
     if isinstance(id_or_env, gym.Env):
         env = id_or_env
+    elif isinstance(id_or_env, gymnasium.Env):
+        env = Gymnasium2GymWrapper(id_or_env)
     else:
-        env = gym.make(id_or_env)
+        if use_gymnasium:
+            env = gymnasium.make(id_or_env)
+            env = Gymnasium2GymWrapper(env)
+        else:
+            env = gym.make(id_or_env)
 
     if print_info:
         print_env_info(env)

reproductions/algorithms/atari/a2c/a2c_reproduction.py

@@ -1,4 +1,4 @@
-# Copyright 2021 Sony Group Corporation.
+# Copyright 2021,2022,2023,2024 Sony Group Corporation.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -28,9 +28,9 @@
 
 def run_training(args):
     set_global_seed(args.seed)
-    train_env = build_atari_env(args.env, seed=args.seed)
+    train_env = build_atari_env(args.env, seed=args.seed, use_gymnasium=args.use_gymnasium)
     eval_env = build_atari_env(
-        args.env, test=True, seed=args.seed + 100, render=args.render)
+        args.env, test=True, seed=args.seed + 100, render=args.render, use_gymnasium=args.use_gymnasium)
 
     iteration_num_hook = H.IterationNumHook(timing=100)
 
@@ -56,7 +56,8 @@ def run_training(args):
 def run_showcase(args):
     if args.snapshot_dir is None:
         raise ValueError('Please specify the snapshot dir for showcasing')
-    eval_env = build_atari_env(args.env, test=True, seed=args.seed + 200, render=False)
+    eval_env = build_atari_env(args.env, test=True, seed=args.seed + 200, render=False,
+                               use_gymnasium=args.use_gymnasium)
     config = A.A2CConfig(gpu_id=args.gpu)
     a2c = serializers.load_snapshot(args.snapshot_dir, eval_env, algorithm_kwargs={"config": config})
     if not isinstance(a2c, A.A2C):
@@ -83,6 +84,7 @@ def main():
     parser.add_argument('--save_timing', type=int, default=250000)
     parser.add_argument('--eval_timing', type=int, default=250000)
     parser.add_argument('--showcase_runs', type=int, default=10)
+    parser.add_argument('--use-gymnasium', action='store_true')
 
     args = parser.parse_args()