Merge branch 'master' into feat/set_options

docs/README.md

@@ -8,7 +8,7 @@ This folder contains documentation for the RL baselines.
 #### Install Sphinx and Theme
 Execute this command in the project root:
 ```
-pip install -e .[docs]
+pip install -e ".[docs]"
 ```
 
 #### Building the Docs

docs/guide/custom_policy.rst

@@ -215,7 +215,7 @@ downsampling and "vector" with a single linear layer.
   from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
 
   class CustomCombinedExtractor(BaseFeaturesExtractor):
-      def __init__(self, observation_space: spaces.Dict):
+      def __init__(self, observation_space: gym.spaces.Dict):
           # We do not know features-dim here before going over all the items,
           # so put something dummy for now. PyTorch requires calling
           # nn.Module.__init__ before adding modules

docs/guide/examples.rst

@@ -319,7 +319,7 @@ You can control the evaluation frequency with ``eval_freq`` to monitor your agen
 
   from stable_baselines3 import SAC
   from stable_baselines3.common.callbacks import EvalCallback
-  from stable-baselines3.common.env_util import make_vec_env
+  from stable_baselines3.common.env_util import make_vec_env
 
   env_id = "Pendulum-v1"
   n_training_envs = 1
@@ -330,7 +330,7 @@ You can control the evaluation frequency with ``eval_freq`` to monitor your agen
   os.makedirs(eval_log_dir, exist_ok=True)
 
   # Initialize a vectorized training environment with default parameters
-  train_env = make_vec_env(env_id, n_env=n_training_envs, seed=0)
+  train_env = make_vec_env(env_id, n_envs=n_training_envs, seed=0)
 
   # Separate evaluation env, with different parameters passed via env_kwargs
   # Eval environments can be vectorized to speed up evaluation.

docs/misc/changelog.rst

@@ -4,7 +4,7 @@ Changelog
 ==========
 
 
-Release 2.1.0a0 (WIP)
+Release 2.1.0a3 (WIP)
 --------------------------
 
 Breaking Changes:
@@ -16,6 +16,7 @@ New Features:
 ^^^^^^^^^^^^^
 - Added Python 3.11 support
 - Add options argument to pass to `env.reset()`. Same as seeds logic, options are reset at the end of an episode (@ReHoss)
+- Added Gymnasium 0.29 support (@pseudo-rnd-thoughts)
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^
@@ -25,6 +26,8 @@ New Features:
 
 Bug Fixes:
 ^^^^^^^^^^
+- Relaxed check in logger, that was causing issue on Windows with colorama
+- Fixed off-policy algorithms with continuous float64 actions (see #1145) (@tobirohrer)
 
 Deprecations:
 ^^^^^^^^^^^^^
@@ -33,9 +36,13 @@ Others:
 ^^^^^^^
 - Updated GitHub issue templates
 - Fix typo in gym patch error message (@lukashass)
+- Refactor ``test_spaces.py`` tests
 
 Documentation:
 ^^^^^^^^^^^^^^
+- Fixed callback example (@BertrandDecoster)
+- Fixed policy network example (@kyle-he)
+- Added mobile-env as new community project (@stefanbschneider)
 
 
 Release 2.0.0 (2023-06-22)
@@ -1396,4 +1403,4 @@ And all the contributors:
 @Melanol @qgallouedec @francescoluciano @jlp-ue @burakdmb @timothe-chaumont @honglu2875
 @anand-bala @hughperkins @sidney-tio @AlexPasqua @dominicgkerr @Akhilez @Rocamonde @tobirohrer @ZikangXiong
 @DavyMorgan @luizapozzobon @Bonifatius94 @theSquaredError @harveybellini @DavyMorgan @FieteO @jonasreiher @npit @WeberSamuel @troiganto
-@lutogniew @lbergmann1 @lukashass @ReHoss
+@lutogniew @lbergmann1 @lukashass @BertrandDecoster @pseudo-rnd-thoughts @stefanbschneider @kyle-he @ReHoss

docs/misc/projects.rst

@@ -197,3 +197,18 @@ A simple library for pink noise exploration with deterministic (DDPG / TD3) and
 | Authors: Onno Eberhard, Jakob Hollenstein, Cristina Pinneri, Georg Martius
 | Github: https://github.com/martius-lab/pink-noise-rl
 | Paper: https://openreview.net/forum?id=hQ9V5QN27eS (Oral at ICLR 2023)
+
+
+mobile-env
+----------
+
+An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
+It allows simulating various scenarios with moving users in a cellular network with multiple base stations.
+
+- Written in pure Python, easy to modify and extend, and can be installed directly via PyPI.
+- Implements the standard Gymnasium interface such that it can be used with all common frameworks for reinforcement learning.
+- There are examples for both single-agent and multi-agent RL using either `stable-baselines3` or Ray RLlib.
+
+| Authors: Stefan Schneider, Stefan Werner
+| Github: https://github.com/stefanbschneider/mobile-env
+| Paper: https://ris.uni-paderborn.de/download/30236/30237 (2022 IEEE/IFIP Network Operations and Management Symposium (NOMS))

setup.py

@@ -100,7 +100,7 @@
     packages=[package for package in find_packages() if package.startswith("stable_baselines3")],
     package_data={"stable_baselines3": ["py.typed", "version.txt"]},
     install_requires=[
-        "gymnasium==0.28.1",
+        "gymnasium>=0.28.1,<0.30",
         "numpy>=1.20",
         "torch>=1.13",
         # For saving models

stable_baselines3/common/buffers.py

@@ -207,7 +207,9 @@ def __init__(
         else:
             self.next_observations = np.zeros((self.buffer_size, self.n_envs, *self.obs_shape), dtype=observation_space.dtype)
 
-        self.actions = np.zeros((self.buffer_size, self.n_envs, self.action_dim), dtype=action_space.dtype)
+        self.actions = np.zeros(
+            (self.buffer_size, self.n_envs, self.action_dim), dtype=self._maybe_cast_dtype(action_space.dtype)
+        )
 
         self.rewards = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)
         self.dones = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)
@@ -311,6 +313,21 @@ def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = Non
         )
         return ReplayBufferSamples(*tuple(map(self.to_torch, data)))
 
+    @staticmethod
+    def _maybe_cast_dtype(dtype: np.typing.DTypeLike) -> np.typing.DTypeLike:
+        """
+        Cast `np.float64` action datatype to `np.float32`,
+        keep the others dtype unchanged.
+        See GH#1572 for more information.
+
+        :param dtype: The original action space dtype
+        :return: ``np.float32`` if the dtype was float64,
+            the original dtype otherwise.
+        """
+        if dtype == np.float64:
+            return np.float32
+        return dtype
+
 
 class RolloutBuffer(BaseBuffer):
     """
@@ -543,7 +560,9 @@ def __init__(
             for key, _obs_shape in self.obs_shape.items()
         }
 
-        self.actions = np.zeros((self.buffer_size, self.n_envs, self.action_dim), dtype=action_space.dtype)
+        self.actions = np.zeros(
+            (self.buffer_size, self.n_envs, self.action_dim), dtype=self._maybe_cast_dtype(action_space.dtype)
+        )
         self.rewards = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)
         self.dones = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)
 

stable_baselines3/common/logger.py

@@ -164,8 +164,10 @@ def __init__(self, filename_or_file: Union[str, TextIO], max_length: int = 36):
         if isinstance(filename_or_file, str):
             self.file = open(filename_or_file, "w")
             self.own_file = True
-        elif isinstance(filename_or_file, TextIOBase):
-            self.file = filename_or_file
+        elif isinstance(filename_or_file, TextIOBase) or hasattr(filename_or_file, "write"):
+            # Note: in theory `TextIOBase` check should be sufficient,
+            # in practice, libraries don't always inherit from it, see GH#1598
+            self.file = filename_or_file  # type: ignore[assignment]
             self.own_file = False
         else:
             raise ValueError(f"Expected file or str, got {filename_or_file}")

stable_baselines3/common/monitor.py

@@ -193,7 +193,9 @@ def __init__(
         mode = "w" if override_existing else "a"
         # Prevent newline issue on Windows, see GH issue #692
         self.file_handler = open(filename, f"{mode}t", newline="\n")
-        self.logger = csv.DictWriter(self.file_handler, fieldnames=("r", "l", "t", *extra_keys))
+        self.logger = csv.DictWriter(
+            self.file_handler, fieldnames=("r", "l", "t", *extra_keys)
+        )  # pytype: disable=wrong-arg-types
         if override_existing:
             self.file_handler.write(f"#{json.dumps(header)}\n")
             self.logger.writeheader()

stable_baselines3/dqn/dqn.py

@@ -245,7 +245,7 @@ def predict(
         if not deterministic and np.random.rand() < self.exploration_rate:
             if self.policy.is_vectorized_observation(observation):
                 if isinstance(observation, dict):
-                    n_batch = observation[list(observation.keys())[0]].shape[0]
+                    n_batch = observation[next(iter(observation.keys()))].shape[0]
                 else:
                     n_batch = observation.shape[0]
                 action = np.array([self.action_space.sample() for _ in range(n_batch)])

stable_baselines3/version.txt

@@ -1 +1 @@
-2.1.0a0
+2.1.0a3

tests/test_envs.py

@@ -156,8 +156,6 @@ def patched_step(_action):
         spaces.Box(low=-1000, high=1000, shape=(3,), dtype=np.float32),
         # Too small range
         spaces.Box(low=-0.1, high=0.1, shape=(2,), dtype=np.float32),
-        # Inverted boundaries
-        spaces.Box(low=1, high=-1, shape=(2,), dtype=np.float32),
         # Same boundaries
         spaces.Box(low=1, high=1, shape=(2,), dtype=np.float32),
         # Unbounded action space

tests/test_logger.py

@@ -437,8 +437,9 @@ def test_ep_buffers_stats_window_size(algo, stats_window_size):
     assert model.ep_success_buffer.maxlen == stats_window_size
 
 
-def test_human_output_format_custom_test_io():
-    class DummyTextIO(TextIOBase):
+@pytest.mark.parametrize("base_class", [object, TextIOBase])
+def test_human_output_format_custom_test_io(base_class):
+    class DummyTextIO(base_class):
         def __init__(self) -> None:
             super().__init__()
             self.lines = [[]]

tests/test_spaces.py

@@ -1,63 +1,67 @@
+from dataclasses import dataclass
 from typing import Dict, Optional
 
 import gymnasium as gym
 import numpy as np
 import pytest
 from gymnasium import spaces
+from gymnasium.spaces.space import Space
 
 from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
 from stable_baselines3.common.env_checker import check_env
 from stable_baselines3.common.env_util import make_vec_env
 from stable_baselines3.common.evaluation import evaluate_policy
 
+BOX_SPACE_FLOAT64 = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float64)
+BOX_SPACE_FLOAT32 = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
 
-class DummyMultiDiscreteSpace(gym.Env):
-    def __init__(self, nvec):
-        super().__init__()
-        self.observation_space = spaces.MultiDiscrete(nvec)
-        self.action_space = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
 
-    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict] = None):
-        if seed is not None:
-            super().reset(seed=seed)
-        return self.observation_space.sample(), {}
+@dataclass
+class DummyEnv(gym.Env):
+    observation_space: Space
+    action_space: Space
 
     def step(self, action):
         return self.observation_space.sample(), 0.0, False, False, {}
 
-
-class DummyMultiBinary(gym.Env):
-    def __init__(self, n):
-        super().__init__()
-        self.observation_space = spaces.MultiBinary(n)
-        self.action_space = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
-
     def reset(self, *, seed: Optional[int] = None, options: Optional[Dict] = None):
         if seed is not None:
             super().reset(seed=seed)
         return self.observation_space.sample(), {}
 
-    def step(self, action):
-        return self.observation_space.sample(), 0.0, False, False, {}
 
-
-class DummyMultidimensionalAction(gym.Env):
+class DummyMultidimensionalAction(DummyEnv):
     def __init__(self):
-        super().__init__()
-        self.observation_space = spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32)
-        self.action_space = spaces.Box(low=-1, high=1, shape=(2, 2), dtype=np.float32)
+        super().__init__(
+            BOX_SPACE_FLOAT32,
+            spaces.Box(low=-1, high=1, shape=(2, 2), dtype=np.float32),
+        )
 
-    def reset(self, *, seed: Optional[int] = None, options: Optional[Dict] = None):
-        if seed is not None:
-            super().reset(seed=seed)
-        return self.observation_space.sample(), {}
 
-    def step(self, action):
-        return self.observation_space.sample(), 0.0, False, False, {}
+class DummyMultiBinary(DummyEnv):
+    def __init__(self, n):
+        super().__init__(
+            spaces.MultiBinary(n),
+            BOX_SPACE_FLOAT32,
+        )
+
+
+class DummyMultiDiscreteSpace(DummyEnv):
+    def __init__(self, nvec):
+        super().__init__(
+            spaces.MultiDiscrete(nvec),
+            BOX_SPACE_FLOAT32,
+        )
 
 
 @pytest.mark.parametrize(
-    "env", [DummyMultiDiscreteSpace([4, 3]), DummyMultiBinary(8), DummyMultiBinary((3, 2)), DummyMultidimensionalAction()]
+    "env",
+    [
+        DummyMultiDiscreteSpace([4, 3]),
+        DummyMultiBinary(8),
+        DummyMultiBinary((3, 2)),
+        DummyMultidimensionalAction(),
+    ],
 )
 def test_env(env):
     # Check the env used for testing
@@ -127,3 +131,40 @@ def test_discrete_obs_space(model_class, env):
     else:
         kwargs = dict(n_steps=256)
     model_class("MlpPolicy", env, **kwargs).learn(256)
+
+
+@pytest.mark.parametrize("model_class", [SAC, TD3, PPO, DDPG, A2C])
+@pytest.mark.parametrize(
+    "obs_space",
+    [
+        BOX_SPACE_FLOAT32,
+        BOX_SPACE_FLOAT64,
+        spaces.Dict({"a": BOX_SPACE_FLOAT32, "b": BOX_SPACE_FLOAT32}),
+        spaces.Dict({"a": BOX_SPACE_FLOAT32, "b": BOX_SPACE_FLOAT64}),
+    ],
+)
+@pytest.mark.parametrize(
+    "action_space",
+    [
+        BOX_SPACE_FLOAT32,
+        BOX_SPACE_FLOAT64,
+    ],
+)
+def test_float64_action_space(model_class, obs_space, action_space):
+    env = DummyEnv(obs_space, action_space)
+    env = gym.wrappers.TimeLimit(env, max_episode_steps=200)
+    if isinstance(env.observation_space, spaces.Dict):
+        policy = "MultiInputPolicy"
+    else:
+        policy = "MlpPolicy"
+
+    if model_class in [PPO, A2C]:
+        kwargs = dict(n_steps=64, policy_kwargs=dict(net_arch=[12]))
+    else:
+        kwargs = dict(learning_starts=60, policy_kwargs=dict(net_arch=[12]))
+
+    model = model_class(policy, env, **kwargs)
+    model.learn(64)
+    initial_obs, _ = env.reset()
+    action, _ = model.predict(initial_obs, deterministic=False)
+    assert action.dtype == env.action_space.dtype