Add the argument dtype (default to float32) to the noise (#1301)

* Fixed noise to return float32

* Updated changelog

* Fixed test to use numpy arrays instead of python floats

* Sorted imports for tests

* Added dtype to constructor

* Removed dtype parameter for VectorizedActionNoise

* __init__ -> None; Capitalize and period in docstring when needed; fix dtype type hint; dtype in docstring

* fix dtype type hint

* Update version

* Clarify changelog [skip ci]

* empty commit to run ci

* Update docs/misc/changelog.rst

---------

Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>

docs/misc/changelog.rst

@@ -27,6 +27,7 @@ New Features:
 Bug Fixes:
 ^^^^^^^^^^
 - Fixed Atari wrapper that missed the reset condition (@luizapozzobon)
+- Added the argument ``dtype`` (default to ``float32``) to the noise for consistency with gym action (@sidney-tio)
 - Fixed PPO train/n_updates metric not accounting for early stopping (@adamfrly)
 
 Deprecations:

stable_baselines3/common/noise.py

@@ -3,6 +3,7 @@ from abc import ABC, abstractmethod
 from typing import Iterable, List, Optional
 
 import numpy as np
+from numpy.typing import DTypeLike
 
 
 class ActionNoise(ABC):
@@ -15,7 +16,7 @@ class ActionNoise(ABC):
 
     def reset(self) -> None:
         """
-        call end of episode reset for the noise
+        Call end of episode reset for the noise
         """
         pass
 
@@ -26,19 +27,21 @@ class ActionNoise(ABC):
 
 class NormalActionNoise(ActionNoise):
     """
-    A Gaussian action noise
+    A Gaussian action noise.
 
-    :param mean: the mean value of the noise
-    :param sigma: the scale of the noise (std here)
+    :param mean: Mean value of the noise
+    :param sigma: Scale of the noise (std here)
+    :param dtype: Type of the output noise
     """
 
-    def __init__(self, mean: np.ndarray, sigma: np.ndarray):
+    def __init__(self, mean: np.ndarray, sigma: np.ndarray, dtype: DTypeLike = np.float32) -> None:
         self._mu = mean
         self._sigma = sigma
+        self._dtype = dtype
         super().__init__()
 
     def __call__(self) -> np.ndarray:
-        return np.random.normal(self._mu, self._sigma)
+        return np.random.normal(self._mu, self._sigma).astype(self._dtype)
 
     def __repr__(self) -> str:
         return f"NormalActionNoise(mu={self._mu}, sigma={self._sigma})"
@@ -50,11 +53,12 @@ class OrnsteinUhlenbeckActionNoise(ActionNoise):
 
     Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
 
-    :param mean: the mean of the noise
-    :param sigma: the scale of the noise
-    :param theta: the rate of mean reversion
-    :param dt: the timestep for the noise
-    :param initial_noise: the initial value for the noise output, (if None: 0)
+    :param mean: Mean of the noise
+    :param sigma: Scale of the noise
+    :param theta: Rate of mean reversion
+    :param dt: Timestep for the noise
+    :param initial_noise: Initial value for the noise output, (if None: 0)
+    :param dtype: Type of the output noise
     """
 
     def __init__(
@@ -64,11 +68,13 @@ class OrnsteinUhlenbeckActionNoise(ActionNoise):
         theta: float = 0.15,
         dt: float = 1e-2,
         initial_noise: Optional[np.ndarray] = None,
-    ):
+        dtype: DTypeLike = np.float32,
+    ) -> None:
         self._theta = theta
         self._mu = mean
         self._sigma = sigma
         self._dt = dt
+        self._dtype = dtype
         self.initial_noise = initial_noise
         self.noise_prev = np.zeros_like(self._mu)
         self.reset()
@@ -81,7 +87,7 @@ class OrnsteinUhlenbeckActionNoise(ActionNoise):
             + self._sigma * np.sqrt(self._dt) * np.random.normal(size=self._mu.shape)
         )
         self.noise_prev = noise
-        return noise
+        return noise.astype(self._dtype)
 
     def reset(self) -> None:
         """
@@ -97,11 +103,11 @@ class VectorizedActionNoise(ActionNoise):
     """
     A Vectorized action noise for parallel environments.
 
-    :param base_noise: ActionNoise The noise generator to use
-    :param n_envs: The number of parallel environments
+    :param base_noise: Noise generator to use
+    :param n_envs: Number of parallel environments
     """
 
-    def __init__(self, base_noise: ActionNoise, n_envs: int):
+    def __init__(self, base_noise: ActionNoise, n_envs: int) -> None:
         try:
             self.n_envs = int(n_envs)
             assert self.n_envs > 0
@@ -113,9 +119,9 @@ class VectorizedActionNoise(ActionNoise):
 
     def reset(self, indices: Optional[Iterable[int]] = None) -> None:
         """
-        Reset all the noise processes, or those listed in indices
+        Reset all the noise processes, or those listed in indices.
 
-        :param indices: Optional[Iterable[int]] The indices to reset. Default: None.
+        :param indices: The indices to reset. Default: None.
             If the parameter is None, then all processes are reset to their initial position.
         """
         if indices is None:
@@ -129,7 +135,7 @@ class VectorizedActionNoise(ActionNoise):
 
     def __call__(self) -> np.ndarray:
         """
-        Generate and stack the action noise from each noise object
+        Generate and stack the action noise from each noise object.
         """
         noise = np.stack([noise() for noise in self.noises])
         return noise

tests/test_deterministic.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pytest
 
 from stable_baselines3 import A2C, DQN, PPO, SAC, TD3
@@ -15,7 +16,9 @@ def test_deterministic_training_common(algo):
     kwargs = {"policy_kwargs": dict(net_arch=[64])}
     env_id = "Pendulum-v1"
     if algo in [TD3, SAC]:
-        kwargs.update({"action_noise": NormalActionNoise(0.0, 0.1), "learning_starts": 100, "train_freq": 4})
+        kwargs.update(
+            {"action_noise": NormalActionNoise(np.zeros(1), 0.1 * np.ones(1)), "learning_starts": 100, "train_freq": 4}
+        )
     else:
         if algo == DQN:
             env_id = "CartPole-v1"