Remove unnecessary SDE resampling in PPO update

.github/workflows/ci.yml

@@ -32,7 +32,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         # cpu version of pytorch
-        pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cpu
+        pip install torch==2.3.1 --index-url https://download.pytorch.org/whl/cpu
 
         # Install Atari Roms
         pip install autorom

Makefile

@@ -20,7 +20,7 @@ lint:
 	# see https://www.flake8rules.com/
 	ruff check ${LINT_PATHS} --select=E9,F63,F7,F82 --output-format=full
 	# exit-zero treats all errors as warnings.
-	ruff check ${LINT_PATHS} --exit-zero
+	ruff check ${LINT_PATHS} --exit-zero --output-format=concise
 
 format:
 	# Sort imports

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 2.4.0a3 (WIP)
+Release 2.4.0a4 (WIP)
 --------------------------
 
 Breaking Changes:
@@ -19,6 +19,7 @@ Bug Fixes:
 - Cast type in compute gae method to avoid error when using torch compile (@amjames)
 - ``CallbackList`` now sets the ``.parent`` attribute of child callbacks to its own ``.parent``. (will-maclean)
 - Fixed error when loading a model that has ``net_arch`` manually set to ``None``   (@jak3122)
+- Set requirement numpy<2.0 until PyTorch is compatible (https://github.com/pytorch/pytorch/issues/107302)
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^
@@ -35,6 +36,8 @@ Deprecations:
 Others:
 ^^^^^^^
 - Fixed various typos (@cschindlbeck)
+- Remove unnecessary SDE noise resampling in PPO update (@brn-dev)
+- Updated PyTorch version on CI to 2.3.1
 
 Bug Fixes:
 ^^^^^^^^^^
@@ -1664,3 +1667,4 @@ And all the contributors:
 @DavyMorgan @luizapozzobon @Bonifatius94 @theSquaredError @harveybellini @DavyMorgan @FieteO @jonasreiher @npit @WeberSamuel @troiganto
 @lutogniew @lbergmann1 @lukashass @BertrandDecoster @pseudo-rnd-thoughts @stefanbschneider @kyle-he @PatrickHelm @corentinlger
 @marekm4 @stagoverflow @rushitnshah @markscsmith @NickLucche @cschindlbeck @peteole @jak3122 @will-maclean
+@brn-dev

setup.py

@@ -101,7 +101,7 @@
     package_data={"stable_baselines3": ["py.typed", "version.txt"]},
     install_requires=[
         "gymnasium>=0.28.1,<0.30",
-        "numpy>=1.20",
+        "numpy>=1.20,<2.0",  # PyTorch not compatible https://github.com/pytorch/pytorch/issues/107302
         "torch>=1.13",
         # For saving models
         "cloudpickle",

stable_baselines3/ppo/ppo.py

@@ -210,10 +210,6 @@ def train(self) -> None:
                     # Convert discrete action from float to long
                     actions = rollout_data.actions.long().flatten()
 
-                # Re-sample the noise matrix because the log_std has changed
-                if self.use_sde:
-                    self.policy.reset_noise(self.batch_size)
-
                 values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions)
                 values = values.flatten()
                 # Normalize advantage

stable_baselines3/version.txt

@@ -1 +1 @@
-2.4.0a3
+2.4.0a4

tests/test_save_load.py

@@ -791,11 +791,11 @@ def test_cast_lr_schedule(tmp_path):
     # Note: for recent version of numpy, np.float64 is a subclass of float
     # so we need to use type here
     # assert isinstance(model.lr_schedule(1.0), float)
-    assert type(model.lr_schedule(1.0)) is float  # noqa: E721
+    assert type(model.lr_schedule(1.0)) is float
     assert np.allclose(model.lr_schedule(0.5), 0.5 * np.sin(1.0))
     model.save(tmp_path / "ppo.zip")
     model = PPO.load(tmp_path / "ppo.zip")
-    assert type(model.lr_schedule(1.0)) is float  # noqa: E721
+    assert type(model.lr_schedule(1.0)) is float
     assert np.allclose(model.lr_schedule(0.5), 0.5 * np.sin(1.0))