Upgrade gym (#613)

fixes some deprecation warnings due to new changes in gym version 0.23:
- use `env.np_random.integers` instead of `env.np_random.randint`
- support `seed` and `return_info` arguments for reset (addresses https://github.com/thu-ml/tianshou/issues/605)

examples/atari/atari_wrapper.py

@@ -32,7 +32,10 @@ class NoopResetEnv(gym.Wrapper):
 
     def reset(self):
         self.env.reset()
-        noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
+        if hasattr(self.unwrapped.np_random, "integers"):
+            noops = self.unwrapped.np_random.integers(1, self.noop_max + 1)
+        else:
+            noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
         for _ in range(noops):
             obs, _, done, _ = self.env.step(self.noop_action)
             if done:

setup.py

@@ -15,7 +15,7 @@ def get_version() -> str:
 
 def get_install_requires() -> str:
     return [
-        "gym>=0.15.4",
+        "gym>=0.23.1",
         "tqdm",
         "numpy>1.16.0",  # https://github.com/numpy/numpy/issues/12793
         "tensorboard>=2.5.0",

test/base/env.py

@@ -79,11 +79,16 @@ class MyTestEnv(gym.Env):
         self.rng = np.random.RandomState(seed)
         return [seed]
 
-    def reset(self, state=0):
+    def reset(self, state=0, seed=None, return_info=False):
+        if seed is not None:
+            self.rng = np.random.RandomState(seed)
         self.done = False
         self.do_sleep()
         self.index = state
-        return self._get_state()
+        if return_info:
+            return self._get_state(), {'key': 1, 'env': self}
+        else:
+            return self._get_state()
 
     def _get_reward(self):
         """Generate a non-scalar reward if ma_rew is True."""

test/base/test_collector.py

@@ -15,6 +15,11 @@ from tianshou.data import (
 from tianshou.env import DummyVectorEnv, SubprocVectorEnv
 from tianshou.policy import BasePolicy
 
+try:
+    import envpool
+except ImportError:
+    envpool = None
+
 if __name__ == '__main__':
     from env import MyTestEnv, NXEnv
 else:  # pytest
@@ -23,7 +28,7 @@ else:  # pytest
 
 class MyPolicy(BasePolicy):
 
-    def __init__(self, dict_state=False, need_state=True):
+    def __init__(self, dict_state=False, need_state=True, action_shape=None):
         """
         :param bool dict_state: if the observation of the environment is a dict
         :param bool need_state: if the policy needs the hidden state (for RNN)
@@ -31,6 +36,7 @@ class MyPolicy(BasePolicy):
         super().__init__()
         self.dict_state = dict_state
         self.need_state = need_state
+        self.action_shape = action_shape
 
     def forward(self, batch, state=None):
         if self.need_state:
@@ -39,8 +45,12 @@ class MyPolicy(BasePolicy):
             else:
                 state += 1
         if self.dict_state:
-            return Batch(act=np.ones(len(batch.obs['index'])), state=state)
-        return Batch(act=np.ones(len(batch.obs)), state=state)
+            action_shape = self.action_shape if self.action_shape else len(
+                batch.obs['index']
+            )
+            return Batch(act=np.ones(action_shape), state=state)
+        action_shape = self.action_shape if self.action_shape else len(batch.obs)
+        return Batch(act=np.ones(action_shape), state=state)
 
     def learn(self):
         pass
@@ -77,7 +87,8 @@ class Logger:
             return Batch()
 
 
-def test_collector():
+@pytest.mark.parametrize("gym_reset_kwargs", [None, dict(return_info=True)])
+def test_collector(gym_reset_kwargs):
     writer = SummaryWriter('log/collector')
     logger = Logger(writer)
     env_fns = [lambda x=i: MyTestEnv(size=x, sleep=0) for i in [2, 3, 4, 5]]
@@ -86,52 +97,102 @@ def test_collector():
     dum = DummyVectorEnv(env_fns)
     policy = MyPolicy()
     env = env_fns[0]()
-    c0 = Collector(policy, env, ReplayBuffer(size=100), logger.preprocess_fn)
-    c0.collect(n_step=3)
+    c0 = Collector(
+        policy,
+        env,
+        ReplayBuffer(size=100),
+        logger.preprocess_fn,
+    )
+    c0.collect(n_step=3, gym_reset_kwargs=gym_reset_kwargs)
     assert len(c0.buffer) == 3
     assert np.allclose(c0.buffer.obs[:4, 0], [0, 1, 0, 0])
     assert np.allclose(c0.buffer[:].obs_next[..., 0], [1, 2, 1])
-    c0.collect(n_episode=3)
+    keys = np.zeros(100)
+    keys[:3] = 1
+    assert np.allclose(c0.buffer.info["key"], keys)
+    for e in c0.buffer.info["env"][:3]:
+        assert isinstance(e, MyTestEnv)
+    assert np.allclose(c0.buffer.info["env_id"], 0)
+    rews = np.zeros(100)
+    rews[:3] = [0, 1, 0]
+    assert np.allclose(c0.buffer.info["rew"], rews)
+    c0.collect(n_episode=3, gym_reset_kwargs=gym_reset_kwargs)
     assert len(c0.buffer) == 8
     assert np.allclose(c0.buffer.obs[:10, 0], [0, 1, 0, 1, 0, 1, 0, 1, 0, 0])
     assert np.allclose(c0.buffer[:].obs_next[..., 0], [1, 2, 1, 2, 1, 2, 1, 2])
-    c0.collect(n_step=3, random=True)
+    assert np.allclose(c0.buffer.info["key"][:8], 1)
+    for e in c0.buffer.info["env"][:8]:
+        assert isinstance(e, MyTestEnv)
+    assert np.allclose(c0.buffer.info["env_id"][:8], 0)
+    assert np.allclose(c0.buffer.info["rew"][:8], [0, 1, 0, 1, 0, 1, 0, 1])
+    c0.collect(n_step=3, random=True, gym_reset_kwargs=gym_reset_kwargs)
+
     c1 = Collector(
         policy, venv, VectorReplayBuffer(total_size=100, buffer_num=4),
         logger.preprocess_fn
     )
-    c1.collect(n_step=8)
+    c1.collect(n_step=8, gym_reset_kwargs=gym_reset_kwargs)
     obs = np.zeros(100)
-    obs[[0, 1, 25, 26, 50, 51, 75, 76]] = [0, 1, 0, 1, 0, 1, 0, 1]
-
+    valid_indices = [0, 1, 25, 26, 50, 51, 75, 76]
+    obs[valid_indices] = [0, 1, 0, 1, 0, 1, 0, 1]
     assert np.allclose(c1.buffer.obs[:, 0], obs)
     assert np.allclose(c1.buffer[:].obs_next[..., 0], [1, 2, 1, 2, 1, 2, 1, 2])
-    c1.collect(n_episode=4)
+    keys = np.zeros(100)
+    keys[valid_indices] = [1, 1, 1, 1, 1, 1, 1, 1]
+    assert np.allclose(c1.buffer.info["key"], keys)
+    for e in c1.buffer.info["env"][valid_indices]:
+        assert isinstance(e, MyTestEnv)
+    env_ids = np.zeros(100)
+    env_ids[valid_indices] = [0, 0, 1, 1, 2, 2, 3, 3]
+    assert np.allclose(c1.buffer.info["env_id"], env_ids)
+    rews = np.zeros(100)
+    rews[valid_indices] = [0, 1, 0, 0, 0, 0, 0, 0]
+    assert np.allclose(c1.buffer.info["rew"], rews)
+    c1.collect(n_episode=4, gym_reset_kwargs=gym_reset_kwargs)
     assert len(c1.buffer) == 16
+    valid_indices = [2, 3, 27, 52, 53, 77, 78, 79]
     obs[[2, 3, 27, 52, 53, 77, 78, 79]] = [0, 1, 2, 2, 3, 2, 3, 4]
     assert np.allclose(c1.buffer.obs[:, 0], obs)
     assert np.allclose(
         c1.buffer[:].obs_next[..., 0],
         [1, 2, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5]
     )
-    c1.collect(n_episode=4, random=True)
+    keys[valid_indices] = [1, 1, 1, 1, 1, 1, 1, 1]
+    assert np.allclose(c1.buffer.info["key"], keys)
+    for e in c1.buffer.info["env"][valid_indices]:
+        assert isinstance(e, MyTestEnv)
+    env_ids[valid_indices] = [0, 0, 1, 2, 2, 3, 3, 3]
+    assert np.allclose(c1.buffer.info["env_id"], env_ids)
+    rews[valid_indices] = [0, 1, 1, 0, 1, 0, 0, 1]
+    assert np.allclose(c1.buffer.info["rew"], rews)
+    c1.collect(n_episode=4, random=True, gym_reset_kwargs=gym_reset_kwargs)
+
     c2 = Collector(
         policy, dum, VectorReplayBuffer(total_size=100, buffer_num=4),
         logger.preprocess_fn
     )
-    c2.collect(n_episode=7)
+    c2.collect(n_episode=7, gym_reset_kwargs=gym_reset_kwargs)
     obs1 = obs.copy()
     obs1[[4, 5, 28, 29, 30]] = [0, 1, 0, 1, 2]
     obs2 = obs.copy()
     obs2[[28, 29, 30, 54, 55, 56, 57]] = [0, 1, 2, 0, 1, 2, 3]
     c2obs = c2.buffer.obs[:, 0]
     assert np.all(c2obs == obs1) or np.all(c2obs == obs2)
-    c2.reset_env()
+    c2.reset_env(gym_reset_kwargs=gym_reset_kwargs)
     c2.reset_buffer()
-    assert c2.collect(n_episode=8)['n/ep'] == 8
-    obs[[4, 5, 28, 29, 30, 54, 55, 56, 57]] = [0, 1, 0, 1, 2, 0, 1, 2, 3]
+    assert c2.collect(n_episode=8, gym_reset_kwargs=gym_reset_kwargs)['n/ep'] == 8
+    valid_indices = [4, 5, 28, 29, 30, 54, 55, 56, 57]
+    obs[valid_indices] = [0, 1, 0, 1, 2, 0, 1, 2, 3]
     assert np.all(c2.buffer.obs[:, 0] == obs)
-    c2.collect(n_episode=4, random=True)
+    keys[valid_indices] = [1, 1, 1, 1, 1, 1, 1, 1, 1]
+    assert np.allclose(c2.buffer.info["key"], keys)
+    for e in c2.buffer.info["env"][valid_indices]:
+        assert isinstance(e, MyTestEnv)
+    env_ids[valid_indices] = [0, 0, 1, 1, 1, 2, 2, 2, 2]
+    assert np.allclose(c2.buffer.info["env_id"], env_ids)
+    rews[valid_indices] = [0, 1, 0, 0, 1, 0, 0, 0, 1]
+    assert np.allclose(c2.buffer.info["rew"], rews)
+    c2.collect(n_episode=4, random=True, gym_reset_kwargs=gym_reset_kwargs)
 
     # test corner case
     with pytest.raises(TypeError):
@@ -147,11 +208,12 @@ def test_collector():
             [lambda i=x: NXEnv(i, obs_type) for x in [5, 10, 15, 20]]
         )
         c3 = Collector(policy, envs, VectorReplayBuffer(total_size=100, buffer_num=4))
-        c3.collect(n_step=6)
+        c3.collect(n_step=6, gym_reset_kwargs=gym_reset_kwargs)
         assert c3.buffer.obs.dtype == object
 
 
-def test_collector_with_async():
+@pytest.mark.parametrize("gym_reset_kwargs", [None, dict(return_info=True)])
+def test_collector_with_async(gym_reset_kwargs):
     env_lens = [2, 3, 4, 5]
     writer = SummaryWriter('log/async_collector')
     logger = Logger(writer)
@@ -163,12 +225,14 @@ def test_collector_with_async():
     policy = MyPolicy()
     bufsize = 60
     c1 = AsyncCollector(
-        policy, venv, VectorReplayBuffer(total_size=bufsize * 4, buffer_num=4),
-        logger.preprocess_fn
+        policy,
+        venv,
+        VectorReplayBuffer(total_size=bufsize * 4, buffer_num=4),
+        logger.preprocess_fn,
     )
     ptr = [0, 0, 0, 0]
     for n_episode in tqdm.trange(1, 30, desc="test async n_episode"):
-        result = c1.collect(n_episode=n_episode)
+        result = c1.collect(n_episode=n_episode, gym_reset_kwargs=gym_reset_kwargs)
         assert result["n/ep"] >= n_episode
         # check buffer data, obs and obs_next, env_id
         for i, count in enumerate(np.bincount(result["lens"], minlength=6)[2:]):
@@ -183,7 +247,7 @@ def test_collector_with_async():
             assert np.all(buf.obs_next[indices].reshape(count, env_len) == seq + 1)
     # test async n_step, for now the buffer should be full of data
     for n_step in tqdm.trange(1, 15, desc="test async n_step"):
-        result = c1.collect(n_step=n_step)
+        result = c1.collect(n_step=n_step, gym_reset_kwargs=gym_reset_kwargs)
         assert result["n/st"] >= n_step
         for i in range(4):
             env_len = i + 2
@@ -618,9 +682,29 @@ def test_collector_with_atari_setting():
         assert np.allclose(result2[key], result[key])
 
 
+@pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform")
+def test_collector_envpool_gym_reset_return_info():
+    envs = envpool.make_gym("Pendulum-v0", num_envs=4, gym_reset_return_info=True)
+    policy = MyPolicy(action_shape=(len(envs), 1))
+
+    c0 = Collector(
+        policy,
+        envs,
+        VectorReplayBuffer(len(envs) * 10, len(envs)),
+        exploration_noise=True
+    )
+    c0.collect(n_step=8)
+    env_ids = np.zeros(len(envs) * 10)
+    env_ids[[0, 1, 10, 11, 20, 21, 30, 31]] = [0, 0, 1, 1, 2, 2, 3, 3]
+    assert np.allclose(c0.buffer.info["env_id"], env_ids)
+
+
 if __name__ == '__main__':
-    test_collector()
+    test_collector(gym_reset_kwargs=None)
+    test_collector(gym_reset_kwargs=dict(return_info=True))
     test_collector_with_dict_state()
     test_collector_with_ma()
     test_collector_with_atari_setting()
-    test_collector_with_async()
+    test_collector_with_async(gym_reset_kwargs=None)
+    test_collector_with_async(gym_reset_kwargs=dict(return_info=True))
+    test_collector_envpool_gym_reset_return_info()

test/base/test_env.py

@@ -222,6 +222,18 @@ def test_env_obs_dtype():
         assert obs.dtype == object
 
 
+def test_env_reset_optional_kwargs(size=10000, num=8):
+    env_fns = [lambda i=i: MyTestEnv(size=i) for i in range(size, size + num)]
+    test_cls = [DummyVectorEnv, SubprocVectorEnv, ShmemVectorEnv]
+    if has_ray():
+        test_cls += [RayVectorEnv]
+    for cls in test_cls:
+        v = cls(env_fns, wait_num=num // 2, timeout=1e-3)
+        _, info = v.reset(seed=1, return_info=True)
+        assert len(info) == len(env_fns)
+        assert isinstance(info[0], dict)
+
+
 def run_align_norm_obs(raw_env, train_env, test_env, action_list):
     eps = np.finfo(np.float32).eps.item()
     raw_obs, train_obs = [raw_env.reset()], [train_env.reset()]
@@ -319,11 +331,25 @@ def test_venv_wrapper_envpool():
     run_align_norm_obs(raw, train, test, actions)
 
 
-if __name__ == "__main__":
+@pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform")
+def test_venv_wrapper_envpool_gym_reset_return_info():
+    num_envs = 4
+    env = VectorEnvNormObs(
+        envpool.make_gym("Ant-v3", num_envs=num_envs, gym_reset_return_info=True)
+    )
+    obs, info = env.reset()
+    assert obs.shape[0] == num_envs
+    for _, v in info.items():
+        if not isinstance(v, dict):
+            assert v.shape[0] == num_envs
+
+
+if __name__ == '__main__':
     test_venv_norm_obs()
     test_venv_wrapper_envpool()
     test_env_obs_dtype()
     test_vecenv()
     test_async_env()
     test_async_check_id()
+    test_env_reset_optional_kwargs()
     test_gym_wrappers()

tianshou/data/collector.py

@@ -100,18 +100,24 @@ class Collector(object):
                 )
         self.buffer = buffer
 
-    def reset(self, reset_buffer: bool = True) -> None:
+    def reset(
+        self,
+        reset_buffer: bool = True,
+        gym_reset_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> None:
         """Reset the environment, statistics, current data and possibly replay memory.
 
         :param bool reset_buffer: if true, reset the replay buffer that is attached
             to the collector.
+        :param gym_reset_kwargs: extra keyword arguments to pass into the environment's
+            reset function. Defaults to None (extra keyword arguments)
         """
         # use empty Batch for "state" so that self.data supports slicing
         # convert empty Batch to None when passing data to policy
         self.data = Batch(
             obs={}, act={}, rew={}, done={}, obs_next={}, info={}, policy={}
         )
-        self.reset_env()
+        self.reset_env(gym_reset_kwargs)
         if reset_buffer:
             self.reset_buffer()
         self.reset_stat()
@@ -124,12 +130,27 @@ class Collector(object):
         """Reset the data buffer."""
         self.buffer.reset(keep_statistics=keep_statistics)
 
-    def reset_env(self) -> None:
+    def reset_env(self, gym_reset_kwargs: Optional[Dict[str, Any]] = None) -> None:
         """Reset all of the environments."""
-        obs = self.env.reset()
-        if self.preprocess_fn:
-            obs = self.preprocess_fn(obs=obs,
-                                     env_id=np.arange(self.env_num)).get("obs", obs)
+        gym_reset_kwargs = gym_reset_kwargs if gym_reset_kwargs else {}
+        rval = self.env.reset(**gym_reset_kwargs)
+        returns_info = isinstance(rval, (tuple, list)) and len(rval) == 2 and (
+            isinstance(rval[1], dict) or isinstance(rval[1][0], dict)  # type: ignore
+        )
+        if returns_info:
+            obs, info = rval
+            if self.preprocess_fn:
+                processed_data = self.preprocess_fn(
+                    obs=obs, info=info, env_id=np.arange(self.env_num)
+                )
+                obs = processed_data.get("obs", obs)
+                info = processed_data.get("info", info)
+                self.data.info = info
+        else:
+            obs = rval
+            if self.preprocess_fn:
+                obs = self.preprocess_fn(obs=obs, env_id=np.arange(self.env_num
+                                                                   )).get("obs", obs)
         self.data.obs = obs
 
     def _reset_state(self, id: Union[int, List[int]]) -> None:
@@ -143,6 +164,33 @@ class Collector(object):
             elif isinstance(state, Batch):
                 state.empty_(id)
 
+    def _reset_env_with_ids(
+        self,
+        local_ids: Union[List[int], np.ndarray],
+        global_ids: Union[List[int], np.ndarray],
+        gym_reset_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        gym_reset_kwargs = gym_reset_kwargs if gym_reset_kwargs else {}
+        rval = self.env.reset(global_ids, **gym_reset_kwargs)
+        returns_info = isinstance(rval, (tuple, list)) and len(rval) == 2 and (
+            isinstance(rval[1], dict) or isinstance(rval[1][0], dict)  # type: ignore
+        )
+        if returns_info:
+            obs_reset, info = rval
+            if self.preprocess_fn:
+                processed_data = self.preprocess_fn(
+                    obs=obs_reset, info=info, env_id=global_ids
+                )
+                obs_reset = processed_data.get("obs", obs_reset)
+                info = processed_data.get("info", info)
+            self.data.info[local_ids] = info
+        else:
+            obs_reset = rval
+            if self.preprocess_fn:
+                obs_reset = self.preprocess_fn(obs=obs_reset, env_id=global_ids
+                                               ).get("obs", obs_reset)
+        self.data.obs_next[local_ids] = obs_reset
+
     def collect(
         self,
         n_step: Optional[int] = None,
@@ -150,6 +198,7 @@ class Collector(object):
         random: bool = False,
         render: Optional[float] = None,
         no_grad: bool = True,
+        gym_reset_kwargs: Optional[Dict[str, Any]] = None,
     ) -> Dict[str, Any]:
         """Collect a specified number of step or episode.
 
@@ -165,6 +214,8 @@ class Collector(object):
             Default to None (no rendering).
         :param bool no_grad: whether to retain gradient in policy.forward(). Default to
             True (no gradient retaining).
+        :param gym_reset_kwargs: extra keyword arguments to pass into the environment's
+            reset function. Defaults to None (extra keyword arguments)
 
         .. note::
 
@@ -288,12 +339,9 @@ class Collector(object):
                 episode_start_indices.append(ep_idx[env_ind_local])
                 # now we copy obs_next to obs, but since there might be
                 # finished episodes, we have to reset finished envs first.
-                obs_reset = self.env.reset(env_ind_global)
-                if self.preprocess_fn:
-                    obs_reset = self.preprocess_fn(
-                        obs=obs_reset, env_id=env_ind_global
-                    ).get("obs", obs_reset)
-                self.data.obs_next[env_ind_local] = obs_reset
+                self._reset_env_with_ids(
+                    env_ind_local, env_ind_global, gym_reset_kwargs
+                )
                 for i in env_ind_local:
                     self._reset_state(i)
 
@@ -367,10 +415,16 @@ class AsyncCollector(Collector):
     ) -> None:
         # assert env.is_async
         warnings.warn("Using async setting may collect extra transitions into buffer.")
-        super().__init__(policy, env, buffer, preprocess_fn, exploration_noise)
+        super().__init__(
+            policy,
+            env,
+            buffer,
+            preprocess_fn,
+            exploration_noise,
+        )
 
-    def reset_env(self) -> None:
-        super().reset_env()
+    def reset_env(self, gym_reset_kwargs: Optional[Dict[str, Any]] = None) -> None:
+        super().reset_env(gym_reset_kwargs)
         self._ready_env_ids = np.arange(self.env_num)
 
     def collect(
@@ -380,6 +434,7 @@ class AsyncCollector(Collector):
         random: bool = False,
         render: Optional[float] = None,
         no_grad: bool = True,
+        gym_reset_kwargs: Optional[Dict[str, Any]] = None,
     ) -> Dict[str, Any]:
         """Collect a specified number of step or episode with async env setting.
 
@@ -395,6 +450,8 @@ class AsyncCollector(Collector):
             Default to None (no rendering).
         :param bool no_grad: whether to retain gradient in policy.forward(). Default to
             True (no gradient retaining).
+        :param gym_reset_kwargs: extra keyword arguments to pass into the environment's
+            reset function. Defaults to None (extra keyword arguments)
 
         .. note::
 
@@ -528,12 +585,9 @@ class AsyncCollector(Collector):
                 episode_start_indices.append(ep_idx[env_ind_local])
                 # now we copy obs_next to obs, but since there might be
                 # finished episodes, we have to reset finished envs first.
-                obs_reset = self.env.reset(env_ind_global)
-                if self.preprocess_fn:
-                    obs_reset = self.preprocess_fn(
-                        obs=obs_reset, env_id=env_ind_global
-                    ).get("obs", obs_reset)
-                self.data.obs_next[env_ind_local] = obs_reset
+                self._reset_env_with_ids(
+                    env_ind_local, env_ind_global, gym_reset_kwargs
+                )
                 for i in env_ind_local:
                     self._reset_state(i)
 

tianshou/env/pettingzoo_env.py

@@ -1,5 +1,5 @@
 from abc import ABC
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, List, Tuple, Union
 
 import gym.spaces
 from pettingzoo.utils.env import AECEnv
@@ -55,11 +55,11 @@ class PettingZooEnv(AECEnv, ABC):
 
         self.reset()
 
-    def reset(self, *args: Any, **kwargs: Any) -> dict:
+    def reset(self, *args: Any, **kwargs: Any) -> Union[dict, Tuple[dict, dict]]:
         self.env.reset(*args, **kwargs)
-        observation = self.env.observe(self.env.agent_selection)
+        observation, _, _, info = self.env.last(self)
         if isinstance(observation, dict) and 'action_mask' in observation:
-            return {
+            observation_dict = {
                 'agent_id': self.env.agent_selection,
                 'obs': observation['observation'],
                 'mask':
@@ -67,13 +67,21 @@ class PettingZooEnv(AECEnv, ABC):
             }
         else:
             if isinstance(self.action_space, gym.spaces.Discrete):
-                return {
+                observation_dict = {
                     'agent_id': self.env.agent_selection,
                     'obs': observation,
                     'mask': [True] * self.env.action_space(self.env.agent_selection).n
                 }
             else:
-                return {'agent_id': self.env.agent_selection, 'obs': observation}
+                observation_dict = {
+                    'agent_id': self.env.agent_selection,
+                    'obs': observation,
+                }
+
+        if "return_info" in kwargs and kwargs["return_info"]:
+            return observation_dict, info
+        else:
+            return observation_dict
 
     def step(self, action: Any) -> Tuple[Dict, List[int], bool, Dict]:
         self.env.step(action)

tianshou/env/venv_wrappers.py

@@ -37,11 +37,12 @@ class VectorEnvWrapper(BaseVectorEnv):
     ) -> None:
         return self.venv.set_env_attr(key, value, id)
 
-    # TODO: compatible issue with reset -> (obs, info)
     def reset(
-        self, id: Optional[Union[int, List[int], np.ndarray]] = None
-    ) -> np.ndarray:
-        return self.venv.reset(id)
+        self,
+        id: Optional[Union[int, List[int], np.ndarray]] = None,
+        **kwargs: Any,
+    ) -> Union[np.ndarray, Tuple[np.ndarray, List[dict]]]:
+        return self.venv.reset(id, **kwargs)
 
     def step(
         self,
@@ -86,14 +87,33 @@ class VectorEnvNormObs(VectorEnvWrapper):
         self.clip_max = clip_obs
         self.eps = epsilon
 
-    # TODO: compatible issue with reset -> (obs, info)
     def reset(
-        self, id: Optional[Union[int, List[int], np.ndarray]] = None
-    ) -> np.ndarray:
-        obs = self.venv.reset(id)
+        self,
+        id: Optional[Union[int, List[int], np.ndarray]] = None,
+        **kwargs: Any,
+    ) -> Union[np.ndarray, Tuple[np.ndarray, List[dict]]]:
+        retval = self.venv.reset(id, **kwargs)
+        reset_returns_info = isinstance(
+            retval, (tuple, list)
+        ) and len(retval) == 2 and isinstance(retval[1], dict)
+        if reset_returns_info:
+            obs, info = retval
+        else:
+            obs = retval
+
+        if isinstance(obs, tuple):
+            raise TypeError(
+                "Tuple observation space is not supported. ",
+                "Please change it to array or dict space",
+            )
+
         if self.obs_rms and self.update_obs_rms:
             self.obs_rms.update(obs)
-        return self._norm_obs(obs)
+        obs = self._norm_obs(obs)
+        if reset_returns_info:
+            return obs, info
+        else:
+            return obs
 
     def step(
         self,

tianshou/env/venvs.py

@@ -181,10 +181,11 @@ class BaseVectorEnv(object):
             assert i in self.ready_id, \
                 f"Can only interact with ready environments {self.ready_id}."
 
-    # TODO: compatible issue with reset -> (obs, info)
     def reset(
-        self, id: Optional[Union[int, List[int], np.ndarray]] = None
-    ) -> np.ndarray:
+        self,
+        id: Optional[Union[int, List[int], np.ndarray]] = None,
+        **kwargs: Any,
+    ) -> Union[np.ndarray, Tuple[np.ndarray, List[dict]]]:
         """Reset the state of some envs and return initial observations.
 
         If id is None, reset the state of all the environments and return
@@ -195,15 +196,35 @@ class BaseVectorEnv(object):
         id = self._wrap_id(id)
         if self.is_async:
             self._assert_id(id)
+
         # send(None) == reset() in worker
         for i in id:
-            self.workers[i].send(None)
-        obs_list = [self.workers[i].recv() for i in id]
+            self.workers[i].send(None, **kwargs)
+        ret_list = [self.workers[i].recv() for i in id]
+
+        reset_returns_info = isinstance(ret_list[0], (tuple, list)) and len(
+            ret_list[0]
+        ) == 2 and isinstance(ret_list[0][1], dict)
+        if reset_returns_info:
+            obs_list = [r[0] for r in ret_list]
+        else:
+            obs_list = ret_list
+
+        if isinstance(obs_list[0], tuple):
+            raise TypeError(
+                "Tuple observation space is not supported. ",
+                "Please change it to array or dict space",
+            )
         try:
             obs = np.stack(obs_list)
         except ValueError:  # different len(obs)
             obs = np.array(obs_list, dtype=object)
-        return obs
+
+        if reset_returns_info:
+            infos = [r[1] for r in ret_list]
+            return obs, infos  # type: ignore
+        else:
+            return obs
 
     def step(
         self,
@@ -248,7 +269,7 @@ class BaseVectorEnv(object):
                 self.workers[j].send(action[i])
             result = []
             for j in id:
-                obs, rew, done, info = self.workers[j].recv()
+                obs, rew, done, info = self.workers[j].recv()  # type: ignore
                 info["env_id"] = j
                 result.append((obs, rew, done, info))
         else:
@@ -270,7 +291,7 @@ class BaseVectorEnv(object):
                 waiting_index = self.waiting_conn.index(conn)
                 self.waiting_conn.pop(waiting_index)
                 env_id = self.waiting_id.pop(waiting_index)
-                obs, rew, done, info = conn.recv()
+                obs, rew, done, info = conn.recv()  # type: ignore
                 info["env_id"] = env_id
                 result.append((obs, rew, done, info))
                 self.ready_id.append(env_id)

tianshou/env/worker/base.py

@@ -14,7 +14,7 @@ class EnvWorker(ABC):
         self._env_fn = env_fn
         self.is_closed = False
         self.result: Union[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
-                           np.ndarray]
+                           Tuple[np.ndarray, dict], np.ndarray]
         self.action_space = self.get_env_attr("action_space")  # noqa: B009
         self.is_reset = False
 
@@ -47,7 +47,8 @@ class EnvWorker(ABC):
 
     def recv(
         self
-    ) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], np.ndarray]:
+    ) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], Tuple[
+        np.ndarray, dict], np.ndarray]:  # noqa:E125
         """Receive result from low-level worker.
 
         If the last "send" function sends a NULL action, it only returns a
@@ -63,9 +64,9 @@ class EnvWorker(ABC):
                 self.result = self.get_result()  # type: ignore
         return self.result
 
-    def reset(self) -> np.ndarray:
-        self.send(None)
-        return self.recv()  # type: ignore
+    @abstractmethod
+    def reset(self, **kwargs: Any) -> Union[np.ndarray, Tuple[np.ndarray, dict]]:
+        pass
 
     def step(
         self, action: np.ndarray

tianshou/env/worker/dummy.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, List, Optional
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import gym
 import numpy as np
@@ -19,8 +19,10 @@ class DummyEnvWorker(EnvWorker):
     def set_env_attr(self, key: str, value: Any) -> None:
         setattr(self.env, key, value)
 
-    def reset(self) -> Any:
-        return self.env.reset()
+    def reset(self, **kwargs: Any) -> Union[np.ndarray, Tuple[np.ndarray, dict]]:
+        if "seed" in kwargs:
+            super().seed(kwargs["seed"])
+        return self.env.reset(**kwargs)
 
     @staticmethod
     def wait(  # type: ignore
@@ -29,15 +31,19 @@ class DummyEnvWorker(EnvWorker):
         # Sequential EnvWorker objects are always ready
         return workers
 
-    def send(self, action: Optional[np.ndarray]) -> None:
+    def send(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
         if action is None:
-            self.result = self.env.reset()  # type: ignore
+            self.result = self.env.reset(**kwargs)
         else:
             self.result = self.env.step(action)  # type: ignore
 
-    def seed(self, seed: Optional[int] = None) -> List[int]:
+    def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
         super().seed(seed)
-        return self.env.seed(seed)
+        try:
+            return self.env.seed(seed)
+        except NotImplementedError:
+            self.env.reset(seed=seed)
+            return [seed]  # type: ignore
 
     def render(self, **kwargs: Any) -> Any:
         return self.env.render(**kwargs)

tianshou/env/worker/ray.py

@@ -35,8 +35,10 @@ class RayEnvWorker(EnvWorker):
     def set_env_attr(self, key: str, value: Any) -> None:
         ray.get(self.env.set_env_attr.remote(key, value))
 
-    def reset(self) -> Any:
-        return ray.get(self.env.reset.remote())
+    def reset(self, **kwargs: Any) -> Any:
+        if "seed" in kwargs:
+            super().seed(kwargs["seed"])
+        return ray.get(self.env.reset.remote(**kwargs))
 
     @staticmethod
     def wait(  # type: ignore
@@ -46,10 +48,10 @@ class RayEnvWorker(EnvWorker):
         ready_results, _ = ray.wait(results, num_returns=wait_num, timeout=timeout)
         return [workers[results.index(result)] for result in ready_results]
 
-    def send(self, action: Optional[np.ndarray]) -> None:
-        # self.action is actually a handle
+    def send(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
+        # self.result is actually a handle
         if action is None:
-            self.result = self.env.reset.remote()
+            self.result = self.env.reset.remote(**kwargs)
         else:
             self.result = self.env.step.remote(action)
 
@@ -58,9 +60,13 @@ class RayEnvWorker(EnvWorker):
     ) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], np.ndarray]:
         return ray.get(self.result)  # type: ignore
 
-    def seed(self, seed: Optional[int] = None) -> List[int]:
+    def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
         super().seed(seed)
-        return ray.get(self.env.seed.remote(seed))
+        try:
+            return ray.get(self.env.seed.remote(seed))
+        except NotImplementedError:
+            self.env.reset.remote(seed=seed)
+            return None
 
     def render(self, **kwargs: Any) -> Any:
         return ray.get(self.env.render.remote(**kwargs))

tianshou/env/worker/subproc.py

@@ -86,17 +86,27 @@ def _worker(
                 p.close()
                 break
             if cmd == "step":
-                if data is None:  # reset
-                    obs = env.reset()
-                else:
-                    obs, reward, done, info = env.step(data)
+                obs, reward, done, info = env.step(data)
                 if obs_bufs is not None:
                     _encode_obs(obs, obs_bufs)
                     obs = None
-                if data is None:
-                    p.send(obs)
+                p.send((obs, reward, done, info))
+            elif cmd == "reset":
+                retval = env.reset(**data)
+                reset_returns_info = isinstance(
+                    retval, (tuple, list)
+                ) and len(retval) == 2 and isinstance(retval[1], dict)
+                if reset_returns_info:
+                    obs, info = retval
                 else:
-                    p.send((obs, reward, done, info))
+                    obs = retval
+                if obs_bufs is not None:
+                    _encode_obs(obs, obs_bufs)
+                    obs = None
+                if reset_returns_info:
+                    p.send((obs, info))
+                else:
+                    p.send(obs)
             elif cmd == "close":
                 p.send(env.close())
                 p.close()
@@ -104,7 +114,11 @@ def _worker(
             elif cmd == "render":
                 p.send(env.render(**data) if hasattr(env, "render") else None)
             elif cmd == "seed":
-                p.send(env.seed(data) if hasattr(env, "seed") else None)
+                if hasattr(env, "seed"):
+                    p.send(env.seed(data))
+                else:
+                    env.reset(seed=data)
+                    p.send(None)
             elif cmd == "getattr":
                 p.send(getattr(env, data) if hasattr(env, data) else None)
             elif cmd == "setattr":
@@ -140,7 +154,6 @@ class SubprocEnvWorker(EnvWorker):
         self.process = Process(target=_worker, args=args, daemon=True)
         self.process.start()
         self.child_remote.close()
-        self.is_reset = False
         super().__init__(env_fn)
 
     def get_env_attr(self, key: str) -> Any:
@@ -186,14 +199,25 @@ class SubprocEnvWorker(EnvWorker):
             remain_conns = [conn for conn in remain_conns if conn not in ready_conns]
         return [workers[conns.index(con)] for con in ready_conns]
 
-    def send(self, action: Optional[np.ndarray]) -> None:
-        self.parent_remote.send(["step", action])
+    def send(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
+        if action is None:
+            if "seed" in kwargs:
+                super().seed(kwargs["seed"])
+            self.parent_remote.send(["reset", kwargs])
+        else:
+            self.parent_remote.send(["step", action])
 
     def recv(
         self
-    ) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], np.ndarray]:
+    ) -> Union[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], Tuple[
+        np.ndarray, dict], np.ndarray]:  # noqa:E125
         result = self.parent_remote.recv()
         if isinstance(result, tuple):
+            if len(result) == 2:
+                obs, info = result
+                if self.share_memory:
+                    obs = self._decode_obs()
+                return obs, info
             obs, rew, done, info = result
             if self.share_memory:
                 obs = self._decode_obs()
@@ -204,6 +228,23 @@ class SubprocEnvWorker(EnvWorker):
                 obs = self._decode_obs()
             return obs
 
+    def reset(self, **kwargs: Any) -> Union[np.ndarray, Tuple[np.ndarray, dict]]:
+        if "seed" in kwargs:
+            super().seed(kwargs["seed"])
+        self.parent_remote.send(["reset", kwargs])
+
+        result = self.parent_remote.recv()
+        if isinstance(result, tuple):
+            obs, info = result
+            if self.share_memory:
+                obs = self._decode_obs()
+            return obs, info
+        else:
+            obs = result
+            if self.share_memory:
+                obs = self._decode_obs()
+            return obs
+
     def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
         super().seed(seed)
         self.parent_remote.send(["seed", seed])