SamplingConfig: support for batch_size=None

examples/mujoco/mujoco_reinforce_hl.py

@@ -29,7 +29,7 @@ def main(
     step_per_epoch: int = 30000,
     step_per_collect: int = 2048,
     repeat_per_collect: int = 1,
-    batch_size: int = 16,
+    batch_size: int | None = None,
     training_num: int = 10,
     test_num: int = 10,
     rew_norm: bool = True,

tianshou/highlevel/config.py

@@ -37,12 +37,17 @@ class SamplingConfig(ToStringMixin):
     an explanation of epoch semantics.
     """
 
-    batch_size: int = 64
+    batch_size: int | None = 64
     """for off-policy algorithms, this is the number of environment steps/transitions to sample
     from the buffer for a gradient update; for on-policy algorithms, its use is algorithm-specific.
     On-policy algorithms use the full buffer that was collected in the preceding collection step
     but they may use this parameter to perform the gradient update using mini-batches of this size
     (causing the gradient to be less accurate, a form of regularization).
+
+    ``batch_size=None`` means that the full buffer is used for the gradient update. This doesn't
+    make much sense for off-policy algorithms and is not recommended then. For on-policy or offline algorithms,
+    this means that the full buffer is used for the gradient update (no mini-batching), and
+    may make sense in some cases.
     """
 
     num_train_envs: int = -1

tianshou/utils/net/common.py

@@ -448,7 +448,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return x
 
 
-#TODO: fix docstring
+# TODO: fix docstring
 class BranchingNet(NetBase[Any]):
     """Branching dual Q network.