vec_envs fix seed() causing a reset (#1486)

* `dummy_vec_env` fix `seed()` causing a reset

* rename `seed`

* fixes

* bug fix

* fix seed return type

* Cleanup seeding, add test and remove compat wrapper

* Update env checker and tests

* Add deterministic test for make_vec_env

---------

Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 2.0.0a8 (WIP)
+Release 2.0.0a9 (WIP)
 --------------------------
 
 **Gymnasium support**
@@ -22,6 +22,7 @@ Breaking Changes:
 - Renamed environment output observations in ``evaluate_policy`` to prevent shadowing the input observations during callbacks (@npit)
 - Upgraded wrappers and custom environment to Gymnasium
 - Refined the ``HumanOutputFormat`` file check: now it verifies if the object is an instance of ``io.TextIOBase`` instead of only checking for the presence of a ``write`` method.
+- Because of new Gym API (0.26+), the random seed passed to ``vec_env.seed(seed=seed)`` will only be effective after then ``env.reset()`` call.
 
 New Features:
 ^^^^^^^^^^^^^
@@ -55,6 +56,7 @@ Others:
 - Fixed ``stable_baselines3/common/vec_env/base_vec_env.py`` type hints
 - Fixed ``stable_baselines3/common/vec_env/vec_frame_stack.py`` type hints
 - Fixed ``stable_baselines3/common/vec_env/dummy_vec_env.py`` type hints
+- Fixed ``stable_baselines3/common/vec_env/subproc_vec_env.py`` type hints
 - Upgraded docker images to use mamba/micromamba and CUDA 11.7
 - Updated env checker to reflect what subset of Gymnasium is supported and improve GoalEnv checks
 - Improve type annotation of wrappers

pyproject.toml

@@ -43,7 +43,6 @@ exclude = """(?x)(
     | stable_baselines3/common/save_util.py$
     | stable_baselines3/common/utils.py$
     | stable_baselines3/common/vec_env/__init__.py$
-    | stable_baselines3/common/vec_env/subproc_vec_env.py$
     | stable_baselines3/common/vec_env/vec_normalize.py$
     | stable_baselines3/common/vec_env/vec_transpose.py$
     | stable_baselines3/common/vec_env/vec_video_recorder.py$

stable_baselines3/common/env_checker.py

@@ -398,6 +398,11 @@ def check_env(env: gym.Env, warn: bool = True, skip_render_check: bool = True) -
     observation_space = env.observation_space
     action_space = env.action_space
 
+    try:
+        env.reset(seed=0)
+    except TypeError as e:
+        raise TypeError("The reset() method must accept a `seed` parameter") from e
+
     # Warn the user if needed.
     # A warning means that the environment may run but not work properly with Stable Baselines algorithms
     if warn:

stable_baselines3/common/env_util.py

@@ -5,7 +5,6 @@
 
 from stable_baselines3.common.atari_wrappers import AtariWrapper
 from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.utils import compat_gym_seed
 from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv, VecEnv
 from stable_baselines3.common.vec_env.patch_gym import _patch_env
 
@@ -101,7 +100,8 @@ def _init() -> gym.Env:
                 env = _patch_env(env)
 
             if seed is not None:
-                compat_gym_seed(env, seed=seed + rank)
+                # Note: here we only seed the action space
+                # We will seed the env at the next reset
                 env.action_space.seed(seed + rank)
             # Wrap the env in a Monitor wrapper
             # to have additional training information
@@ -122,7 +122,10 @@ def _init() -> gym.Env:
         # Default: use a DummyVecEnv
         vec_env_cls = DummyVecEnv
 
-    return vec_env_cls([make_env(i + start_index) for i in range(n_envs)], **vec_env_kwargs)
+    vec_env = vec_env_cls([make_env(i + start_index) for i in range(n_envs)], **vec_env_kwargs)
+    # Prepare the seeds for the first reset
+    vec_env.seed(seed)
+    return vec_env
 
 
 def make_atari_env(

stable_baselines3/common/noise.py

@@ -131,7 +131,7 @@ def reset(self, indices: Optional[Iterable[int]] = None) -> None:
             self.noises[index].reset()
 
     def __repr__(self) -> str:
-        return f"VecNoise(BaseNoise={repr(self.base_noise)}), n_envs={len(self.noises)})"
+        return f"VecNoise(BaseNoise={self.base_noise!r}), n_envs={len(self.noises)})"
 
     def __call__(self) -> np.ndarray:
         """

stable_baselines3/common/utils.py

@@ -4,7 +4,6 @@
 import random
 import re
 from collections import deque
-from inspect import signature
 from itertools import zip_longest
 from typing import Dict, Iterable, List, Optional, Tuple, Union
 
@@ -549,18 +548,3 @@ def get_system_info(print_info: bool = True) -> Tuple[Dict[str, str], str]:
     if print_info:
         print(env_info_str)
     return env_info, env_info_str
-
-
-def compat_gym_seed(env: GymEnv, seed: int) -> None:
-    """
-    Compatibility helper to seed Gym envs.
-
-    :param env: The Gym environment.
-    :param seed: The seed for the pseudo random generator
-    """
-    if "seed" in signature(env.unwrapped.reset).parameters:
-        # gym >= 0.23.1
-        env.reset(seed=seed)
-    else:
-        # VecEnv and backward compatibility
-        env.seed(seed)

stable_baselines3/common/vec_env/base_vec_env.py

@@ -69,6 +69,14 @@ def __init__(
         self.render_mode = render_mode
         # store info returned by the reset method
         self.reset_infos: List[Dict[str, Any]] = [{} for _ in range(num_envs)]
+        # seeds to be used in the next call to env.reset()
+        self._seeds: List[Optional[int]] = [None for _ in range(num_envs)]
+
+    def _reset_seeds(self) -> None:
+        """
+        Reset the seeds that are going to be used at the next reset.
+        """
+        self._seeds = [None for _ in range(self.num_envs)]
 
     @abstractmethod
     def reset(self) -> VecEnvObs:
@@ -239,17 +247,24 @@ def render(self, mode: Optional[str] = None) -> Optional[np.ndarray]:
             self.env_method("render")
         return None
 
-    @abstractmethod
     def seed(self, seed: Optional[int] = None) -> Sequence[Union[None, int]]:
         """
         Sets the random seeds for all environments, based on a given seed.
         Each individual environment will still get its own seed, by incrementing the given seed.
+        WARNING: since gym 0.26, those seeds will only be passed to the environment
+        at the next reset.
 
         :param seed: The random seed. May be None for completely random seeding.
         :return: Returns a list containing the seeds for each individual env.
             Note that all list elements may be None, if the env does not return anything when being seeded.
         """
-        pass
+        if seed is None:
+            # To ensure that subprocesses have different seeds,
+            # we still populate the seed variable when no argument is passed
+            seed = np.random.randint(0, 2**32 - 1)
+
+        self._seeds = [seed + idx for idx in range(self.num_envs)]
+        return self._seeds
 
     @property
     def unwrapped(self) -> "VecEnv":

stable_baselines3/common/vec_env/dummy_vec_env.py

@@ -1,7 +1,7 @@
 import warnings
 from collections import OrderedDict
 from copy import deepcopy
-from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, Type
 
 import gymnasium as gym
 import numpy as np
@@ -71,21 +71,12 @@ def step_wait(self) -> VecEnvStepReturn:
             self._save_obs(env_idx, obs)
         return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones), deepcopy(self.buf_infos))
 
-    def seed(self, seed: Optional[int] = None) -> Sequence[Union[None, int]]:
-        # Avoid circular import
-        from stable_baselines3.common.utils import compat_gym_seed
-
-        if seed is None:
-            seed = np.random.randint(0, 2**32 - 1)
-        seeds = []
-        for idx, env in enumerate(self.envs):
-            seeds.append(compat_gym_seed(env, seed=seed + idx))  # type: ignore[func-returns-value]
-        return seeds
-
     def reset(self) -> VecEnvObs:
         for env_idx in range(self.num_envs):
-            obs, self.reset_infos[env_idx] = self.envs[env_idx].reset()
+            obs, self.reset_infos[env_idx] = self.envs[env_idx].reset(seed=self._seeds[env_idx])
             self._save_obs(env_idx, obs)
+        # Seeds are only used once
+        self._reset_seeds()
         return self._obs_from_buf()
 
     def close(self) -> None:

stable_baselines3/common/vec_env/subproc_vec_env.py

@@ -1,7 +1,7 @@
 import multiprocessing as mp
 import warnings
 from collections import OrderedDict
-from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Type, Union
 
 import gymnasium as gym
 import numpy as np
@@ -24,11 +24,10 @@ def _worker(
 ) -> None:
     # Import here to avoid a circular import
     from stable_baselines3.common.env_util import is_wrapped
-    from stable_baselines3.common.utils import compat_gym_seed
 
     parent_remote.close()
     env = _patch_env(env_fn_wrapper.var())
-    reset_info = {}
+    reset_info: Optional[Dict[str, Any]] = {}
     while True:
         try:
             cmd, data = remote.recv()
@@ -42,10 +41,8 @@ def _worker(
                     info["terminal_observation"] = observation
                     observation, reset_info = env.reset()
                 remote.send((observation, reward, done, info, reset_info))
-            elif cmd == "seed":
-                remote.send(compat_gym_seed(env, seed=data))
             elif cmd == "reset":
-                observation, reset_info = env.reset()
+                observation, reset_info = env.reset(seed=data)
                 remote.send((observation, reset_info))
             elif cmd == "render":
                 remote.send(env.render())
@@ -61,7 +58,7 @@ def _worker(
             elif cmd == "get_attr":
                 remote.send(getattr(env, data))
             elif cmd == "set_attr":
-                remote.send(setattr(env, data[0], data[1]))
+                remote.send(setattr(env, data[0], data[1]))  # type: ignore[func-returns-value]
             elif cmd == "is_wrapped":
                 remote.send(is_wrapped(env, data))
             else:
@@ -112,7 +109,9 @@ def __init__(self, env_fns: List[Callable[[], gym.Env]], start_method: Optional[
         for work_remote, remote, env_fn in zip(self.work_remotes, self.remotes, env_fns):
             args = (work_remote, remote, CloudpickleWrapper(env_fn))
             # daemon=True: if the main process crashes, we should not cause things to hang
-            process = ctx.Process(target=_worker, args=args, daemon=True)  # pytype:disable=attribute-error
+            # pytype: disable=attribute-error
+            process = ctx.Process(target=_worker, args=args, daemon=True)  # type: ignore[attr-defined]
+            # pytype: enable=attribute-error
             process.start()
             self.processes.append(process)
             work_remote.close()
@@ -135,18 +134,13 @@ def step_wait(self) -> VecEnvStepReturn:
         obs, rews, dones, infos, self.reset_infos = zip(*results)
         return _flatten_obs(obs, self.observation_space), np.stack(rews), np.stack(dones), infos
 
-    def seed(self, seed: Optional[int] = None) -> Sequence[Union[None, int]]:
-        if seed is None:
-            seed = np.random.randint(0, 2**32 - 1)
-        for idx, remote in enumerate(self.remotes):
-            remote.send(("seed", seed + idx))
-        return [remote.recv() for remote in self.remotes]
-
     def reset(self) -> VecEnvObs:
-        for remote in self.remotes:
-            remote.send(("reset", None))
+        for env_idx, remote in enumerate(self.remotes):
+            remote.send(("reset", self._seeds[env_idx]))
         results = [remote.recv() for remote in self.remotes]
         obs, self.reset_infos = zip(*results)
+        # Seeds are only used once
+        self._reset_seeds()
         return _flatten_obs(obs, self.observation_space)
 
     def close(self) -> None:
@@ -235,6 +229,6 @@ def _flatten_obs(obs: Union[List[VecEnvObs], Tuple[VecEnvObs]], space: spaces.Sp
     elif isinstance(space, spaces.Tuple):
         assert isinstance(obs[0], tuple), "non-tuple observation for environment with Tuple observation space"
         obs_len = len(space.spaces)
-        return tuple(np.stack([o[i] for o in obs]) for i in range(obs_len))
+        return tuple(np.stack([o[i] for o in obs]) for i in range(obs_len))  # type: ignore[index]
     else:
-        return np.stack(obs)
+        return np.stack(obs)  # type: ignore[arg-type]

stable_baselines3/version.txt

@@ -1 +1 @@
-2.0.0a8
+2.0.0a9

tests/test_buffers.py

@@ -25,7 +25,7 @@ def __init__(self):
         self._t = 0
         self._ep_length = 100
 
-    def reset(self):
+    def reset(self, *, seed=None, options=None):
         self._t = 0
         obs = self._observations[0]
         return obs, {}
@@ -55,7 +55,7 @@ def __init__(self):
         self._t = 0
         self._ep_length = 100
 
-    def reset(self):
+    def reset(self, seed=None, options=None):
         self._t = 0
         obs = {key: self._observations[0] for key in self.observation_space.spaces.keys()}
         return obs, {}

tests/test_env_checker.py

@@ -23,7 +23,7 @@ def step(self, action):
         info = {}
         return observation, reward, terminated, truncated, info
 
-    def reset(self):
+    def reset(self, seed=None):
         return np.array([1.0, 1.5, 0.5], dtype=self.observation_space.dtype), {}
 
     def render(self):

tests/test_envs.py

@@ -130,8 +130,12 @@ def patched_step(_action):
 def test_non_default_spaces(new_obs_space):
     env = FakeImageEnv()
     env.observation_space = new_obs_space
+
     # Patch methods to avoid errors
-    env.reset = lambda: (new_obs_space.sample(), {})
+    def patched_reset(seed=None):
+        return new_obs_space.sample(), {}
+
+    env.reset = patched_reset
 
     def patched_step(_action):
         return new_obs_space.sample(), 0.0, False, False, {}
@@ -204,7 +208,7 @@ def check_reset_assert_error(env, new_reset_return):
     :param new_reset_return: (Any)
     """
 
-    def wrong_reset():
+    def wrong_reset(seed=None):
         return new_reset_return, {}
 
     # Patch the reset method with a wrong one
@@ -224,10 +228,21 @@ def test_common_failures_reset():
     check_reset_assert_error(env, 1)
 
     # Return only obs (gym < 0.26)
-    env.reset = env.observation_space.sample
+    def wrong_reset(self, seed=None):
+        return env.observation_space.sample()
+
+    env.reset = types.MethodType(wrong_reset, env)
     with pytest.raises(AssertionError):
         check_env(env)
 
+    # No seed parameter (gym < 0.26)
+    def wrong_reset(self):
+        return env.observation_space.sample(), {}
+
+    env.reset = types.MethodType(wrong_reset, env)
+    with pytest.raises(TypeError):
+        check_env(env)
+
     # Return not only the observation
     check_reset_assert_error(env, (env.observation_space.sample(), False))
 
@@ -242,7 +257,7 @@ def test_common_failures_reset():
 
     obs, _ = env.reset()
 
-    def wrong_reset(self):
+    def wrong_reset(self, seed=None):
         return {"img": obs["img"], "vec": obs["img"]}, {}
 
     env.reset = types.MethodType(wrong_reset, env)

tests/test_logger.py

@@ -96,7 +96,7 @@ def read_fn(_format):
             tb_values_logged = []
             for reservoir in [acc.scalars, acc.tensors, acc.images, acc.histograms, acc.compressed_histograms]:
                 for k in reservoir.Keys():
-                    tb_values_logged.append(f"{k}: {str(reservoir.Items(k))}")
+                    tb_values_logged.append(f"{k}: {reservoir.Items(k)!s}")
 
             content = LogContent(_format, tb_values_logged)
             return content
@@ -353,7 +353,7 @@ def __init__(self, delay: float = 0.01):
         self.observation_space = spaces.Box(low=-20.0, high=20.0, shape=(4,), dtype=np.float32)
         self.action_space = spaces.Discrete(2)
 
-    def reset(self):
+    def reset(self, seed=None):
         return self.observation_space.sample(), {}
 
     def step(self, action):

tests/test_predict.py

@@ -30,7 +30,7 @@ def __init__(self):
         self.observation_space = SubClassedBox(-1, 1, shape=(2,), dtype=np.float32)
         self.action_space = SubClassedBox(-1, 1, shape=(2,), dtype=np.float32)
 
-    def reset(self):
+    def reset(self, seed=None):
         return self.observation_space.sample(), {}
 
     def step(self, action):