Cast learning_rate to float lambda for pickle safety when doing model…

….load (DLR-RM#1901)

* create failing test for unpickle error

* Fix learning_rate argument causing failure in weights_only=True if passed a function with non-float types

* Updated with feedback from araffin on PR#1901

* Update test and version

* Update changelog and SBX doc

---------

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

README.md

@@ -85,7 +85,7 @@ Documentation is available online: [https://sb3-contrib.readthedocs.io/](https:/
 
 ## Stable-Baselines Jax (SBX)
 
-[Stable Baselines Jax (SBX)](https://github.com/araffin/sbx) is a proof of concept version of Stable-Baselines3 in Jax.
+[Stable Baselines Jax (SBX)](https://github.com/araffin/sbx) is a proof of concept version of Stable-Baselines3 in Jax, with recent algorithms like DroQ or CrossQ.
 
 It provides a minimal number of features compared to SB3 but can be much faster (up to 20x times!): https://twitter.com/araffin2/status/1590714558628253698
 

docs/guide/algos.rst

@@ -43,7 +43,8 @@ Actions ``gym.spaces``:
 
 .. note::
 
-  More algorithms (like QR-DQN or TQC) are implemented in our :ref:`contrib repo <sb3_contrib>`.
+  More algorithms (like QR-DQN or TQC) are implemented in our :ref:`contrib repo <sb3_contrib>`
+  and in our :ref:`SBX (SB3 + Jax) repo <sbx>` (DroQ, CrossQ, ...).
 
 .. note::
 

docs/guide/sbx.rst

@@ -17,6 +17,7 @@ Implemented algorithms:
 - Deep Q Network (DQN)
 - Twin Delayed DDPG (TD3)
 - Deep Deterministic Policy Gradient (DDPG)
+- Batch Normalization in Deep Reinforcement Learning (CrossQ)
 
 
 As SBX follows SB3 API, it is also compatible with the `RL Zoo <https://github.com/DLR-RM/rl-baselines3-zoo>`_.
@@ -29,16 +30,17 @@ For that you will need to create two files:
   import rl_zoo3
   import rl_zoo3.train
   from rl_zoo3.train import train
-
-  from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, DroQ
+  from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, CrossQ
 
   rl_zoo3.ALGOS["ddpg"] = DDPG
   rl_zoo3.ALGOS["dqn"] = DQN
-  rl_zoo3.ALGOS["droq"] = DroQ
+  # See SBX readme to use DroQ configuration
+  # rl_zoo3.ALGOS["droq"] = DroQ
   rl_zoo3.ALGOS["sac"] = SAC
   rl_zoo3.ALGOS["ppo"] = PPO
   rl_zoo3.ALGOS["td3"] = TD3
   rl_zoo3.ALGOS["tqc"] = TQC
+  rl_zoo3.ALGOS["crossq"] = CrossQ
   rl_zoo3.train.ALGOS = rl_zoo3.ALGOS
   rl_zoo3.exp_manager.ALGOS = rl_zoo3.ALGOS
 
@@ -56,16 +58,17 @@ Then you can call ``python train_sbx.py --algo sac --env Pendulum-v1`` and use t
   import rl_zoo3
   import rl_zoo3.enjoy
   from rl_zoo3.enjoy import enjoy
-
-  from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, DroQ
+  from sbx import DDPG, DQN, PPO, SAC, TD3, TQC, CrossQ
 
   rl_zoo3.ALGOS["ddpg"] = DDPG
   rl_zoo3.ALGOS["dqn"] = DQN
-  rl_zoo3.ALGOS["droq"] = DroQ
+  # See SBX readme to use DroQ configuration
+  # rl_zoo3.ALGOS["droq"] = DroQ
   rl_zoo3.ALGOS["sac"] = SAC
   rl_zoo3.ALGOS["ppo"] = PPO
   rl_zoo3.ALGOS["td3"] = TD3
   rl_zoo3.ALGOS["tqc"] = TQC
+  rl_zoo3.ALGOS["crossq"] = CrossQ
   rl_zoo3.enjoy.ALGOS = rl_zoo3.ALGOS
   rl_zoo3.exp_manager.ALGOS = rl_zoo3.ALGOS
 

docs/misc/changelog.rst

@@ -3,6 +3,18 @@
 Changelog
 ==========
 
+Release 2.3.1 (2024-04-22)
+--------------------------
+
+Bug Fixes:
+^^^^^^^^^^
+- Cast return value of learning rate schedule to float, to avoid issue when loading model because of ``weights_only=True`` (@markscsmith)
+
+Documentation:
+^^^^^^^^^^^^^^
+- Updated SBX documentation (CrossQ and deprecated DroQ)
+
+
 Release 2.3.0 (2024-03-31)
 --------------------------
 
@@ -1593,4 +1605,4 @@ And all the contributors:
 @anand-bala @hughperkins @sidney-tio @AlexPasqua @dominicgkerr @Akhilez @Rocamonde @tobirohrer @ZikangXiong @ReHoss
 @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
+@marekm4 @stagoverflow @rushitnshah @markscsmith

stable_baselines3/common/utils.py

@@ -92,7 +92,9 @@ def get_schedule_fn(value_schedule: Union[Schedule, float]) -> Schedule:
         value_schedule = constant_fn(float(value_schedule))
     else:
         assert callable(value_schedule)
-    return value_schedule
+    # Cast to float to avoid unpickling errors to enable weights_only=True, see GH#1900
+    # Some types are have odd behaviors when part of a Schedule, like numpy floats
+    return lambda progress_remaining: float(value_schedule(progress_remaining))
 
 
 def get_linear_fn(start: float, end: float, end_fraction: float) -> Schedule:

stable_baselines3/version.txt

@@ -1 +1 @@
-2.3.0
+2.3.1

tests/test_save_load.py

@@ -783,3 +783,17 @@ def test_no_resource_warning(tmp_path):
         fp.seek(0)
         model.load_replay_buffer(fp)
         assert not fp.closed
+
+
+def test_cast_lr_schedule(tmp_path):
+    # See GH#1900
+    model = PPO("MlpPolicy", "Pendulum-v1", learning_rate=lambda t: t * np.sin(1.0))
+    # 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 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 np.allclose(model.lr_schedule(0.5), 0.5 * np.sin(1.0))