backup wip

lerobot/common/policies/policy_protocol.py

@@ -0,0 +1,45 @@
+"""A protocol that all policies should follow.
+
+This provides a mechanism for type-hinting and isinstance checks without requiring the policies classes
+subclass a base class.
+
+The protocol structure, method signatures, and docstrings should be used by developers as a reference for
+how to implement new policies.
+"""
+
+from typing import Protocol, runtime_checkable
+
+from torch import Tensor
+
+
+@runtime_checkable
+class Policy(Protocol):
+    """The required interface for implementing a policy."""
+
+    name: str
+
+    def reset(self):
+        """To be called whenever the environment is reset.
+
+        Does things like clearing caches.
+        """
+
+    def forward(self, batch: dict[str, Tensor], **kwargs):
+        """Wired to `select_action`."""
+
+    def select_action(self, batch: dict[str, Tensor], **kwargs):
+        """Return one action to run in the environment (potentially in batch mode).
+
+        When the model uses a history of observations, or outputs a sequence of actions, this method deals
+        with caching.
+        """
+
+    def compute_loss(self, batch: dict[str, Tensor], **kwargs):
+        """Runs the batch through the model and computes the loss for training or validation."""
+
+    def update(self, batch, **kwargs):
+        """Does compute_loss then an optimization step.
+
+        TODO(alexander-soare): We will move the optimization step back into the training loop, so this will
+        disappear.
+        """