Add policy documentation links to policy_kwargs parameter

docs/misc/changelog.rst

@@ -38,6 +38,7 @@ Documentation:
 ^^^^^^^^^^^^^^
 - Added Decisions and Dragons to resources. (@jmacglashan)
 - Updated PyBullet example, now compatible with Gymnasium
+- Added link to policies for ``policy_kwargs`` parameter (@kplers)
 
 Release 2.4.0 (2024-11-18)
 --------------------------
@@ -1738,4 +1739,4 @@ 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 @jmacglashan
+@brn-dev @jmacglashan @kplers

docs/modules/a2c.rst

@@ -78,7 +78,7 @@ Train a A2C agent on ``CartPole-v1`` using 4 environments.
 
   A2C is meant to be run primarily on the CPU, especially when you are not using a CNN. To improve CPU utilization, try turning off the GPU and using ``SubprocVecEnv`` instead of the default ``DummyVecEnv``:
 
-  .. code-block::
+  .. code-block:: python
 
     from stable_baselines3 import A2C
     from stable_baselines3.common.env_util import make_vec_env
@@ -88,7 +88,7 @@ Train a A2C agent on ``CartPole-v1`` using 4 environments.
         env = make_vec_env("CartPole-v1", n_envs=8, vec_env_cls=SubprocVecEnv)
         model = A2C("MlpPolicy", env, device="cpu")
         model.learn(total_timesteps=25_000)
-  
+
   For more information, see :ref:`Vectorized Environments <vec_env>`, `Issue #1245 <https://github.com/DLR-RM/stable-baselines3/issues/1245>`_ or the `Multiprocessing notebook <https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/multiprocessing_rl.ipynb>`_.
 
 
@@ -165,6 +165,8 @@ Parameters
   :inherited-members:
 
 
+.. _a2c_policies:
+
 A2C Policies
 -------------
 

docs/modules/ppo.rst

@@ -92,7 +92,7 @@ Train a PPO agent on ``CartPole-v1`` using 4 environments.
 
   PPO is meant to be run primarily on the CPU, especially when you are not using a CNN. To improve CPU utilization, try turning off the GPU and using ``SubprocVecEnv`` instead of the default ``DummyVecEnv``:
 
-  .. code-block::
+  .. code-block:: python
 
     from stable_baselines3 import PPO
     from stable_baselines3.common.env_util import make_vec_env
@@ -102,7 +102,7 @@ Train a PPO agent on ``CartPole-v1`` using 4 environments.
         env = make_vec_env("CartPole-v1", n_envs=8, vec_env_cls=SubprocVecEnv)
         model = PPO("MlpPolicy", env, device="cpu")
         model.learn(total_timesteps=25_000)
-  
+
   For more information, see :ref:`Vectorized Environments <vec_env>`, `Issue #1245 <https://github.com/DLR-RM/stable-baselines3/issues/1245#issuecomment-1435766949>`_ or the `Multiprocessing notebook <https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/multiprocessing_rl.ipynb>`_.
 
 Results
@@ -178,6 +178,8 @@ Parameters
   :inherited-members:
 
 
+.. _ppo_policies:
+
 PPO Policies
 -------------
 

stable_baselines3/a2c/a2c.py

@@ -48,7 +48,7 @@ class A2C(OnPolicyAlgorithm):
     :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
         the reported success rate, mean episode length, and mean reward over
     :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`a2c_policies`
     :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
         debug messages
     :param seed: Seed for the pseudo random generators

stable_baselines3/ddpg/ddpg.py

@@ -44,7 +44,7 @@ class DDPG(TD3):
     :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
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`ddpg_policies`
     :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
         debug messages
     :param seed: Seed for the pseudo random generators

stable_baselines3/dqn/dqn.py

@@ -53,7 +53,7 @@ class DQN(OffPolicyAlgorithm):
     :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
         the reported success rate, mean episode length, and mean reward over
     :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`dqn_policies`
     :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
         debug messages
     :param seed: Seed for the pseudo random generators

stable_baselines3/ppo/ppo.py

@@ -62,7 +62,7 @@ class PPO(OnPolicyAlgorithm):
     :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
         the reported success rate, mean episode length, and mean reward over
     :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`ppo_policies`
     :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
         debug messages
     :param seed: Seed for the pseudo random generators

stable_baselines3/sac/sac.py

@@ -68,7 +68,7 @@ class SAC(OffPolicyAlgorithm):
     :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
         the reported success rate, mean episode length, and mean reward over
     :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`sac_policies`
     :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
         debug messages
     :param seed: Seed for the pseudo random generators

stable_baselines3/td3/td3.py

@@ -56,7 +56,7 @@ class TD3(OffPolicyAlgorithm):
     :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
         the reported success rate, mean episode length, and mean reward over
     :param tensorboard_log: the log location for tensorboard (if None, no logging)
-    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param policy_kwargs: additional arguments to be passed to the policy on creation. See :ref:`td3_policies`
     :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
         debug messages
     :param seed: Seed for the pseudo random generators