Dictionary Observations (#29)

* Add TQC support for new HER version

* Add dict obs support

* Add support for dict obs

docs/misc/changelog.rst

@@ -3,11 +3,14 @@
 Changelog
 ==========
 
-Release 1.1.0a5 (WIP)
+Release 1.1.0a6 (WIP)
 -------------------------------
 
 Breaking Changes:
 ^^^^^^^^^^^^^^^^^
+- Added support for Dictionary observation spaces (cf SB3 doc)
+- Upgraded to Stable-Baselines3 >= 1.1.0
+- Added proper handling of timeouts for off-policy algorithms (cf SB3 doc)
 
 Bug Fixes:
 ^^^^^^^^^^

docs/modules/qrdqn.rst

@@ -18,6 +18,7 @@ instead of predicting the mean return (DQN).
 
     MlpPolicy
     CnnPolicy
+    MultiInputPolicy
 
 
 Notes
@@ -43,6 +44,7 @@ Discrete      ✔      ✔
 Box           ❌      ✔
 MultiDiscrete ❌      ✔
 MultiBinary   ❌      ✔
+Dict          ❌     ✔️
 ============= ====== ===========
 
 
@@ -149,3 +151,6 @@ QR-DQN Policies
 
 .. autoclass:: CnnPolicy
   :members:
+
+.. autoclass:: MultiInputPolicy
+  :members:

docs/modules/tqc.rst

@@ -18,6 +18,7 @@ It truncates the quantiles predicted by different networks (a bit as it is done
 
     MlpPolicy
     CnnPolicy
+    MultiInputPolicy
 
 
 Notes
@@ -42,6 +43,7 @@ Discrete      ❌      ✔️
 Box           ✔️       ✔️
 MultiDiscrete ❌      ✔️
 MultiBinary   ❌      ✔️
+Dict          ❌     ✔️
 ============= ====== ===========
 
 

sb3_contrib/qrdqn/__init__.py

@@ -1,2 +1,2 @@
-from sb3_contrib.qrdqn.policies import CnnPolicy, MlpPolicy
+from sb3_contrib.qrdqn.policies import CnnPolicy, MlpPolicy, MultiInputPolicy
 from sb3_contrib.qrdqn.qrdqn import QRDQN

sb3_contrib/qrdqn/policies.py

@@ -3,7 +3,13 @@ from typing import Any, Dict, List, Optional, Type
 import gym
 import torch as th
 from stable_baselines3.common.policies import BasePolicy, register_policy
-from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
+from stable_baselines3.common.torch_layers import (
+    BaseFeaturesExtractor,
+    CombinedExtractor,
+    FlattenExtractor,
+    NatureCNN,
+    create_mlp,
+)
 from stable_baselines3.common.type_aliases import Schedule
 from torch import nn
 
@@ -245,5 +251,54 @@ class CnnPolicy(QRDQNPolicy):
         )
 
 
+class MultiInputPolicy(QRDQNPolicy):
+    """
+    Policy class for QR-DQN when using dict observations as input.
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param n_quantiles: Number of quantiles
+    :param net_arch: The specification of the network architecture.
+    :param activation_fn: Activation function
+    :param features_extractor_class: Features extractor to use.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        lr_schedule: Schedule,
+        n_quantiles: int = 200,
+        net_arch: Optional[List[int]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        features_extractor_class: Type[BaseFeaturesExtractor] = CombinedExtractor,
+        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super(MultiInputPolicy, self).__init__(
+            observation_space,
+            action_space,
+            lr_schedule,
+            n_quantiles,
+            net_arch,
+            activation_fn,
+            features_extractor_class,
+            features_extractor_kwargs,
+            normalize_images,
+            optimizer_class,
+            optimizer_kwargs,
+        )
+
+
 register_policy("MlpPolicy", MlpPolicy)
 register_policy("CnnPolicy", CnnPolicy)
+register_policy("MultiInputPolicy", MultiInputPolicy)

sb3_contrib/qrdqn/qrdqn.py

@@ -4,6 +4,7 @@ import gym
 import numpy as np
 import torch as th
 from stable_baselines3.common import logger
+from stable_baselines3.common.buffers import ReplayBuffer
 from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
 from stable_baselines3.common.preprocessing import maybe_transpose
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
@@ -34,6 +35,9 @@ class QRDQN(OffPolicyAlgorithm):
         (see ``train_freq`` and ``n_episodes_rollout``)
         Set to ``-1`` means to do as many gradient steps as steps done in the environment
         during the rollout.
+    :param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
+        If ``None``, it will be automatically selected.
+    :param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
     :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
         at a cost of more complexity.
         See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
@@ -59,13 +63,15 @@ class QRDQN(OffPolicyAlgorithm):
         policy: Union[str, Type[QRDQNPolicy]],
         env: Union[GymEnv, str],
         learning_rate: Union[float, Schedule] = 5e-5,
-        buffer_size: int = 1000000,
+        buffer_size: int = 1000000,  # 1e6
         learning_starts: int = 50000,
         batch_size: Optional[int] = 32,
         tau: float = 1.0,
         gamma: float = 0.99,
         train_freq: int = 4,
         gradient_steps: int = 1,
+        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         target_update_interval: int = 10000,
         exploration_fraction: float = 0.005,
@@ -94,6 +100,8 @@ class QRDQN(OffPolicyAlgorithm):
             train_freq,
             gradient_steps,
             action_noise=None,  # No action noise
+            replay_buffer_class=replay_buffer_class,
+            replay_buffer_kwargs=replay_buffer_kwargs,
             policy_kwargs=policy_kwargs,
             tensorboard_log=tensorboard_log,
             verbose=verbose,
@@ -152,7 +160,7 @@ class QRDQN(OffPolicyAlgorithm):
         self._update_learning_rate(self.policy.optimizer)
 
         losses = []
-        for gradient_step in range(gradient_steps):
+        for _ in range(gradient_steps):
             # Sample replay buffer
             replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
 
@@ -213,7 +221,10 @@ class QRDQN(OffPolicyAlgorithm):
         """
         if not deterministic and np.random.rand() < self.exploration_rate:
             if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
-                n_batch = observation.shape[0]
+                if isinstance(self.observation_space, gym.spaces.Dict):
+                    n_batch = observation[list(observation.keys())[0]].shape[0]
+                else:
+                    n_batch = observation.shape[0]
                 action = np.array([self.action_space.sample() for _ in range(n_batch)])
             else:
                 action = np.array(self.action_space.sample())

sb3_contrib/tqc/__init__.py

@@ -1,2 +1,2 @@
-from sb3_contrib.tqc.policies import CnnPolicy, MlpPolicy
+from sb3_contrib.tqc.policies import CnnPolicy, MlpPolicy, MultiInputPolicy
 from sb3_contrib.tqc.tqc import TQC

sb3_contrib/tqc/policies.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, Dict, List, Optional, Tuple, Type
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 import gym
 import torch as th
@@ -7,11 +7,13 @@ from stable_baselines3.common.policies import BaseModel, BasePolicy, create_sde_
 from stable_baselines3.common.preprocessing import get_action_dim
 from stable_baselines3.common.torch_layers import (
     BaseFeaturesExtractor,
+    CombinedExtractor,
     FlattenExtractor,
     NatureCNN,
     create_mlp,
     get_actor_critic_arch,
 )
+from stable_baselines3.common.type_aliases import Schedule
 from torch import nn as nn
 
 # CAP the standard deviation of the actor
@@ -278,6 +280,8 @@ class TQCPolicy(BasePolicy):
         ``th.optim.Adam`` by default
     :param optimizer_kwargs: Additional keyword arguments,
         excluding the learning rate, to pass to the optimizer
+    :param n_quantiles: Number of quantiles for the critic.
+    :param n_critics: Number of critic networks to create.
     :param share_features_extractor: Whether to share or not the features extractor
         between the actor and the critic (this saves computation time)
     """
@@ -286,8 +290,8 @@ class TQCPolicy(BasePolicy):
         self,
         observation_space: gym.spaces.Space,
         action_space: gym.spaces.Space,
-        lr_schedule: Callable,
+        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
+        net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
         activation_fn: Type[nn.Module] = nn.ReLU,
         use_sde: bool = False,
         log_std_init: float = -3,
@@ -353,7 +357,7 @@ class TQCPolicy(BasePolicy):
 
         self._build(lr_schedule)
 
-    def _build(self, lr_schedule: Callable) -> None:
+    def _build(self, lr_schedule: Schedule) -> None:
         self.actor = self.make_actor()
         self.actor.optimizer = self.optimizer_class(self.actor.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
 
@@ -448,6 +452,8 @@ class CnnPolicy(TQCPolicy):
         ``th.optim.Adam`` by default
     :param optimizer_kwargs: Additional keyword arguments,
         excluding the learning rate, to pass to the optimizer
+    :param n_quantiles: Number of quantiles for the critic.
+    :param n_critics: Number of critic networks to create.
     :param share_features_extractor: Whether to share or not the features extractor
         between the actor and the critic (this saves computation time)
     """
@@ -456,8 +462,8 @@ class CnnPolicy(TQCPolicy):
         self,
         observation_space: gym.spaces.Space,
         action_space: gym.spaces.Space,
-        lr_schedule: Callable,
+        lr_schedule: Schedule,
-        net_arch: Optional[List[int]] = None,
+        net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
         activation_fn: Type[nn.Module] = nn.ReLU,
         use_sde: bool = False,
         log_std_init: float = -3,
@@ -495,5 +501,80 @@ class CnnPolicy(TQCPolicy):
         )
 
 
+class MultiInputPolicy(TQCPolicy):
+    """
+    Policy class (with both actor and critic) for TQC.
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param use_sde: Whether to use State Dependent Exploration or not
+    :param log_std_init: Initial value for the log standard deviation
+    :param sde_net_arch: Network architecture for extracting features
+        when using gSDE. If None, the latent features from the policy will be used.
+        Pass an empty list to use the states as features.
+    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure
+        a positive standard deviation (cf paper). It allows to keep variance
+        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
+    :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.
+    :param features_extractor_class: Features extractor to use.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    :param n_quantiles: Number of quantiles for the critic.
+    :param n_critics: Number of critic networks to create.
+    :param share_features_extractor: Whether to share or not the features extractor
+        between the actor and the critic (this saves computation time)
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        lr_schedule: Schedule,
+        net_arch: Optional[Union[List[int], Dict[str, List[int]]]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        use_sde: bool = False,
+        log_std_init: float = -3,
+        sde_net_arch: Optional[List[int]] = None,
+        use_expln: bool = False,
+        clip_mean: float = 2.0,
+        features_extractor_class: Type[BaseFeaturesExtractor] = CombinedExtractor,
+        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+        n_quantiles: int = 25,
+        n_critics: int = 2,
+        share_features_extractor: bool = True,
+    ):
+        super(MultiInputPolicy, self).__init__(
+            observation_space,
+            action_space,
+            lr_schedule,
+            net_arch,
+            activation_fn,
+            use_sde,
+            log_std_init,
+            sde_net_arch,
+            use_expln,
+            clip_mean,
+            features_extractor_class,
+            features_extractor_kwargs,
+            normalize_images,
+            optimizer_class,
+            optimizer_kwargs,
+            n_quantiles,
+            n_critics,
+            share_features_extractor,
+        )
+
+
 register_policy("MlpPolicy", MlpPolicy)
 register_policy("CnnPolicy", CnnPolicy)
+register_policy("MultiInputPolicy", MultiInputPolicy)

sb3_contrib/tqc/tqc.py

@@ -4,6 +4,7 @@ import gym
 import numpy as np
 import torch as th
 from stable_baselines3.common import logger
+from stable_baselines3.common.buffers import ReplayBuffer
 from stable_baselines3.common.noise import ActionNoise
 from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback
@@ -35,6 +36,9 @@ class TQC(OffPolicyAlgorithm):
     :param gradient_steps: How many gradient update after each step
     :param action_noise: the action noise type (None by default), this can help
         for hard exploration problem. Cf common.noise for the different action noise type.
+    :param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
+        If ``None``, it will be automatically selected.
+    :param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
     :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
         at a cost of more complexity.
         See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
@@ -66,7 +70,7 @@ class TQC(OffPolicyAlgorithm):
         policy: Union[str, Type[TQCPolicy]],
         env: Union[GymEnv, str],
         learning_rate: Union[float, Callable] = 3e-4,
-        buffer_size: int = int(1e6),
+        buffer_size: int = 1000000,  # 1e6
         learning_starts: int = 100,
         batch_size: int = 256,
         tau: float = 0.005,
@@ -74,6 +78,8 @@ class TQC(OffPolicyAlgorithm):
         train_freq: int = 1,
         gradient_steps: int = 1,
         action_noise: Optional[ActionNoise] = None,
+        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         ent_coef: Union[str, float] = "auto",
         target_update_interval: int = 1,
@@ -103,7 +109,9 @@ class TQC(OffPolicyAlgorithm):
             gamma,
             train_freq,
             gradient_steps,
-            action_noise,
+            action_noise=action_noise,
+            replay_buffer_class=replay_buffer_class,
+            replay_buffer_kwargs=replay_buffer_kwargs,
             policy_kwargs=policy_kwargs,
             tensorboard_log=tensorboard_log,
             verbose=verbose,

sb3_contrib/version.txt

@@ -1 +1 @@
-1.1.0a5
+1.1.0a6

setup.cfg

@@ -7,11 +7,8 @@ license_file = LICENSE
 env =
 	PYTHONHASHSEED=0
 filterwarnings =
-		# Tensorboard/Tensorflow warnings
+		# Tensorboard warnings
-		ignore:inspect.getargspec:DeprecationWarning:tensorflow
+		ignore::DeprecationWarning:tensorboard
-		ignore:builtin type EagerTensor has no __module__ attribute:DeprecationWarning
-		ignore:The binary mode of fromstring is deprecated:DeprecationWarning
-		ignore::FutureWarning:tensorflow
 		# Gym warnings
 		ignore:Parameters to load are deprecated.:DeprecationWarning
 		ignore:the imp module is deprecated in favour of importlib:PendingDeprecationWarning

setup.py

@@ -62,7 +62,7 @@ setup(
     packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
     package_data={"sb3_contrib": ["py.typed", "version.txt"]},
     install_requires=[
-        "stable_baselines3>=1.0",
+        "stable_baselines3>=1.1.0a6",
     ],
     description="Contrib package of Stable Baselines3, experimental code.",
     author="Antonin Raffin",

tests/test_cnn.py

@@ -4,7 +4,7 @@ from copy import deepcopy
 import numpy as np
 import pytest
 import torch as th
-from stable_baselines3.common.identity_env import FakeImageEnv
+from stable_baselines3.common.envs import FakeImageEnv
 from stable_baselines3.common.utils import zip_strict
 from stable_baselines3.common.vec_env import VecTransposeImage, is_vecenv_wrapped
 

tests/test_dict_env.py

@@ -0,0 +1,258 @@
+import gym
+import numpy as np
+import pytest
+from gym import spaces
+from stable_baselines3.common.envs import BitFlippingEnv, SimpleMultiObsEnv
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.vec_env import DummyVecEnv, VecFrameStack, VecNormalize
+
+from sb3_contrib import QRDQN, TQC
+
+
+class DummyDictEnv(gym.Env):
+    """Custom Environment for testing purposes only"""
+
+    metadata = {"render.modes": ["human"]}
+
+    def __init__(
+        self,
+        use_discrete_actions=False,
+        channel_last=False,
+        nested_dict_obs=False,
+        vec_only=False,
+    ):
+        super().__init__()
+        if use_discrete_actions:
+            self.action_space = spaces.Discrete(3)
+        else:
+            self.action_space = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
+        N_CHANNELS = 1
+        HEIGHT = 64
+        WIDTH = 64
+
+        if channel_last:
+            obs_shape = (HEIGHT, WIDTH, N_CHANNELS)
+        else:
+            obs_shape = (N_CHANNELS, HEIGHT, WIDTH)
+
+        self.observation_space = spaces.Dict(
+            {
+                # Image obs
+                "img": spaces.Box(low=0, high=255, shape=obs_shape, dtype=np.uint8),
+                # Vector obs
+                "vec": spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32),
+                # Discrete obs
+                "discrete": spaces.Discrete(4),
+            }
+        )
+
+        # For checking consistency with normal MlpPolicy
+        if vec_only:
+            self.observation_space = spaces.Dict(
+                {
+                    # Vector obs
+                    "vec": spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32),
+                }
+            )
+
+        if nested_dict_obs:
+            # Add dictionary observation inside observation space
+            self.observation_space.spaces["nested-dict"] = spaces.Dict({"nested-dict-discrete": spaces.Discrete(4)})
+
+    def seed(self, seed=None):
+        if seed is not None:
+            self.observation_space.seed(seed)
+
+    def step(self, action):
+        reward = 0.0
+        done = False
+        return self.observation_space.sample(), reward, done, {}
+
+    def compute_reward(self, achieved_goal, desired_goal, info):
+        return np.zeros((len(achieved_goal),))
+
+    def reset(self):
+        return self.observation_space.sample()
+
+    def render(self, mode="human"):
+        pass
+
+
+@pytest.mark.parametrize("model_class", [QRDQN, TQC])
+def test_consistency(model_class):
+    """
+    Make sure that dict obs with vector only vs using flatten obs is equivalent.
+    This ensures notable that the network architectures are the same.
+    """
+    use_discrete_actions = model_class == QRDQN
+    dict_env = DummyDictEnv(use_discrete_actions=use_discrete_actions, vec_only=True)
+    dict_env = gym.wrappers.TimeLimit(dict_env, 100)
+    env = gym.wrappers.FlattenObservation(dict_env)
+    dict_env.seed(10)
+    obs = dict_env.reset()
+
+    kwargs = {}
+    n_steps = 256
+
+    if model_class in {}:
+        kwargs = dict(
+            n_steps=128,
+        )
+    else:
+        # Avoid memory error when using replay buffer
+        # Reduce the size of the features and make learning faster
+        kwargs = dict(
+            buffer_size=250,
+            train_freq=8,
+            gradient_steps=1,
+        )
+        if model_class == QRDQN:
+            kwargs["learning_starts"] = 0
+
+    dict_model = model_class("MultiInputPolicy", dict_env, gamma=0.5, seed=1, **kwargs)
+    action_before_learning_1, _ = dict_model.predict(obs, deterministic=True)
+    dict_model.learn(total_timesteps=n_steps)
+
+    normal_model = model_class("MlpPolicy", env, gamma=0.5, seed=1, **kwargs)
+    action_before_learning_2, _ = normal_model.predict(obs["vec"], deterministic=True)
+    normal_model.learn(total_timesteps=n_steps)
+
+    action_1, _ = dict_model.predict(obs, deterministic=True)
+    action_2, _ = normal_model.predict(obs["vec"], deterministic=True)
+
+    assert np.allclose(action_before_learning_1, action_before_learning_2)
+    assert np.allclose(action_1, action_2)
+
+
+@pytest.mark.parametrize("model_class", [QRDQN, TQC])
+@pytest.mark.parametrize("channel_last", [False, True])
+def test_dict_spaces(model_class, channel_last):
+    """
+    Additional tests to check observation space support
+    with mixed observation.
+    """
+    use_discrete_actions = model_class not in [TQC]
+    env = DummyDictEnv(use_discrete_actions=use_discrete_actions, channel_last=channel_last)
+    env = gym.wrappers.TimeLimit(env, 100)
+
+    kwargs = {}
+    n_steps = 256
+
+    if model_class in {}:
+        kwargs = dict(
+            n_steps=128,
+            policy_kwargs=dict(
+                net_arch=[32],
+                features_extractor_kwargs=dict(cnn_output_dim=32),
+            ),
+        )
+    else:
+        # Avoid memory error when using replay buffer
+        # Reduce the size of the features and make learning faster
+        kwargs = dict(
+            buffer_size=250,
+            policy_kwargs=dict(
+                net_arch=[32],
+                features_extractor_kwargs=dict(cnn_output_dim=32),
+                n_quantiles=20,
+            ),
+            train_freq=8,
+            gradient_steps=1,
+        )
+        if model_class == QRDQN:
+            kwargs["learning_starts"] = 0
+
+    model = model_class("MultiInputPolicy", env, gamma=0.5, seed=1, **kwargs)
+
+    model.learn(total_timesteps=n_steps)
+
+    evaluate_policy(model, env, n_eval_episodes=5, warn=False)
+
+
+@pytest.mark.parametrize("model_class", [QRDQN, TQC])
+@pytest.mark.parametrize("channel_last", [False, True])
+def test_dict_vec_framestack(model_class, channel_last):
+    """
+    Additional tests to check observation space support
+    for Dictionary spaces and VecEnvWrapper using MultiInputPolicy.
+    """
+    use_discrete_actions = model_class not in [TQC]
+    channels_order = {"vec": None, "img": "last" if channel_last else "first"}
+    env = DummyVecEnv(
+        [lambda: SimpleMultiObsEnv(random_start=True, discrete_actions=use_discrete_actions, channel_last=channel_last)]
+    )
+
+    env = VecFrameStack(env, n_stack=3, channels_order=channels_order)
+
+    kwargs = {}
+    n_steps = 256
+
+    if model_class in {}:
+        kwargs = dict(
+            n_steps=128,
+            policy_kwargs=dict(
+                net_arch=[32],
+                features_extractor_kwargs=dict(cnn_output_dim=32),
+            ),
+        )
+    else:
+        # Avoid memory error when using replay buffer
+        # Reduce the size of the features and make learning faster
+        kwargs = dict(
+            buffer_size=250,
+            policy_kwargs=dict(
+                net_arch=[32],
+                features_extractor_kwargs=dict(cnn_output_dim=32),
+                n_quantiles=20,
+            ),
+            train_freq=8,
+            gradient_steps=1,
+        )
+        if model_class == QRDQN:
+            kwargs["learning_starts"] = 0
+
+    model = model_class("MultiInputPolicy", env, gamma=0.5, seed=1, **kwargs)
+
+    model.learn(total_timesteps=n_steps)
+
+    evaluate_policy(model, env, n_eval_episodes=5, warn=False)
+
+
+@pytest.mark.parametrize("model_class", [QRDQN, TQC])
+def test_vec_normalize(model_class):
+    """
+    Additional tests to check observation space support
+    for GoalEnv and VecNormalize using MultiInputPolicy.
+    """
+    env = DummyVecEnv([lambda: BitFlippingEnv(n_bits=4, continuous=not (model_class == QRDQN))])
+    env = VecNormalize(env)
+
+    kwargs = {}
+    n_steps = 256
+
+    if model_class in {}:
+        kwargs = dict(
+            n_steps=128,
+            policy_kwargs=dict(
+                net_arch=[32],
+            ),
+        )
+    else:
+        # Avoid memory error when using replay buffer
+        # Reduce the size of the features and make learning faster
+        kwargs = dict(
+            buffer_size=250,
+            policy_kwargs=dict(
+                net_arch=[32],
+            ),
+            train_freq=8,
+            gradient_steps=1,
+        )
+        if model_class == QRDQN:
+            kwargs["learning_starts"] = 0
+
+    model = model_class("MultiInputPolicy", env, gamma=0.5, seed=1, **kwargs)
+
+    model.learn(total_timesteps=n_steps)
+
+    evaluate_policy(model, env, n_eval_episodes=5, warn=False)

tests/test_save_load.py

@@ -8,7 +8,7 @@ import numpy as np
 import pytest
 import torch as th
 from stable_baselines3.common.base_class import BaseAlgorithm
-from stable_baselines3.common.identity_env import FakeImageEnv, IdentityEnv, IdentityEnvBox
+from stable_baselines3.common.envs import FakeImageEnv, IdentityEnv, IdentityEnvBox
 from stable_baselines3.common.utils import get_device
 from stable_baselines3.common.vec_env import DummyVecEnv
 
@@ -243,6 +243,7 @@ def test_save_load_replay_buffer(tmp_path, model_class):
     assert np.allclose(old_replay_buffer.actions, model.replay_buffer.actions)
     assert np.allclose(old_replay_buffer.rewards, model.replay_buffer.rewards)
     assert np.allclose(old_replay_buffer.dones, model.replay_buffer.dones)
+    infos = [[{"TimeLimit.truncated": truncated}] for truncated in old_replay_buffer.timeouts]
 
     # test extending replay buffer
     model.replay_buffer.extend(
@@ -251,6 +252,7 @@ def test_save_load_replay_buffer(tmp_path, model_class):
         old_replay_buffer.actions,
         old_replay_buffer.rewards,
         old_replay_buffer.dones,
+        infos,
     )
 
 

tests/test_wrappers.py

@@ -2,15 +2,13 @@ import gym
 import numpy as np
 import pytest
 from gym import spaces
-from stable_baselines3.common.bit_flipping_env import BitFlippingEnv
 from stable_baselines3.common.env_checker import check_env
+from stable_baselines3.common.envs import BitFlippingEnv
 
 from sb3_contrib.common.wrappers import TimeFeatureWrapper
 
 
 class CustomGoalEnv(gym.GoalEnv):
-    """docstring for CustomGoalEnv."""
-
     def __init__(self):
         super(CustomGoalEnv, self).__init__()
         self.observation_space = spaces.Dict(