[Doc] Better doc on multi-head entropy

Author: vmoens

torchrl/objectives/ppo.py

@@ -104,6 +104,8 @@ class PPOLoss(LossModule):
             * **Scalar**: one value applied to the summed entropy of every action head.
             * **Mapping** ``{head_name: coeff}`` gives an individual coefficient for each action-head's entropy.
             Defaults to ``0.01``.
+
+            See :ref:`ppo_entropy_coefficients` for detailed usage examples and troubleshooting.
         log_explained_variance (bool, optional): if ``True``, the explained variance of the critic
             predictions w.r.t. value targets will be computed and logged as ``"explained_variance"``.
             This can help monitor critic quality during training. Best possible score is 1.0, lower values are worse. Defaults to ``True``.
@@ -217,7 +219,7 @@ class PPOLoss(LossModule):
         >>> action = spec.rand(batch)
         >>> data = TensorDict({"observation": torch.randn(*batch, n_obs),
         ...         "action": action,
-        ...         "sample_log_prob": torch.randn_like(action[..., 1]),
+        ...         "action_log_prob": torch.randn_like(action[..., 1]),
         ...         ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool),
         ...         ("next", "terminated"): torch.zeros(*batch, 1, dtype=torch.bool),
         ...         ("next", "reward"): torch.randn(*batch, 1),
@@ -227,6 +229,8 @@ class PPOLoss(LossModule):
         TensorDict(
             fields={
                 entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
+                explained_variance: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),
+                kl_approx: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                 loss_critic: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                 loss_entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                 loss_objective: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
@@ -279,12 +283,69 @@ class PPOLoss(LossModule):
         ...         next_observation=torch.randn(*batch, n_obs))
         >>> loss_objective.backward()
 
+    **Simple Entropy Coefficient Examples**:
+        >>> # Scalar entropy coefficient (default behavior)
+        >>> loss = PPOLoss(actor, critic, entropy_coeff=0.01)
+        >>>
+        >>> # Per-head entropy coefficients (for composite action spaces)
+        >>> entropy_coeff = {
+        ...     ("agent0", "action_log_prob"): 0.01,  # Low exploration
+        ...     ("agent1", "action_log_prob"): 0.05,  # High exploration
+        ... }
+        >>> loss = PPOLoss(actor, critic, entropy_coeff=entropy_coeff)
+
     .. note::
       There is an exception regarding compatibility with non-tensordict-based modules.
       If the actor network is probabilistic and uses a :class:`~tensordict.nn.distributions.CompositeDistribution`,
       this class must be used with tensordicts and cannot function as a tensordict-independent module.
       This is because composite action spaces inherently rely on the structured representation of data provided by
       tensordicts to handle their actions.
+
+    .. _ppo_entropy_coefficients:
+
+    .. note::
+        **Entropy Bonus and Coefficient Management**
+
+        The entropy bonus encourages exploration by adding the negative entropy of the policy to the loss.
+        This can be configured in two ways:
+
+        **Scalar Coefficient (Default)**: Use a single coefficient for all action heads:
+            >>> loss = PPOLoss(actor, critic, entropy_coeff=0.01)
+
+        **Per-Head Coefficients**: Use different coefficients for different action components:
+            >>> # For a robot with movement and gripper actions
+            >>> entropy_coeff = {
+            ...     ("agent0", "action_log_prob"): 0.01,  # Movement: low exploration
+            ...     ("agent1", "action_log_prob"): 0.05,  # Gripper: high exploration
+            ... }
+            >>> loss = PPOLoss(actor, critic, entropy_coeff=entropy_coeff)
+
+        **Key Requirements**: When using per-head coefficients, you must provide the full nested key
+        path to each action head's log probability (e.g., `("agent0", "action_log_prob")`).
+
+        **Monitoring Entropy Loss**:
+
+        When using composite action spaces, the loss output includes:
+        - `"entropy"`: Summed entropy across all action heads (for logging)
+        - `"composite_entropy"`: Individual entropy values for each action head
+        - `"loss_entropy"`: The weighted entropy loss term
+
+        Example output:
+            >>> result = loss(data)
+            >>> print(result["entropy"])           # Total entropy: 2.34
+            >>> print(result["composite_entropy"]) # Per-head: {"movement": 1.2, "gripper": 1.14}
+            >>> print(result["loss_entropy"])      # Weighted loss: -0.0234
+
+        **Common Issues**:
+
+        **KeyError: "Missing entropy coeff for head 'head_name'"**:
+            - Ensure you provide coefficients for ALL action heads
+            - Use full nested keys: `("head_name", "action_log_prob")`
+            - Check that your action space structure matches the coefficient mapping
+
+        **Incorrect Entropy Calculation**:
+            - Call `set_composite_lp_aggregate(False).set()` before creating your policy
+            - Verify that your action space uses :class:`~tensordict.nn.distributions.CompositeDistribution`
     """
 
     @dataclass
@@ -911,27 +972,37 @@ def _weighted_loss_entropy(
         Otherwise, use the scalar `self.entropy_coeff`.
         The entries in self._entropy_coeff_map require the full nested key to the entropy head.
         """
+        # Mode 1: Use scalar entropy coefficient (default behavior)
         if self._entropy_coeff_map is None:
+            # If entropy is a TensorDict (composite action space), sum all entropy values
             if is_tensor_collection(entropy):
                 entropy = _sum_td_features(entropy)
+            # Apply scalar coefficient: loss = -coeff * entropy (negative for maximization)
             return -self.entropy_coeff * entropy
 
-        loss_term = None  # running sum over heads
-        coeff = 0
+        # Mode 2: Use per-head entropy coefficients (for composite action spaces)
+        loss_term = None  # Initialize running sum over action heads
+        coeff = 0  # Placeholder for coefficient value
+        # Iterate through all entropy heads in the composite action space
         for head_name, entropy_head in entropy.items(
             include_nested=True, leaves_only=True
         ):
             try:
+                # Look up the coefficient for this specific action head
                 coeff = self._entropy_coeff_map[head_name]
             except KeyError as exc:
+                # Provide clear error message if coefficient mapping is incomplete
                 raise KeyError(f"Missing entropy coeff for head '{head_name}'") from exc
+            # Convert coefficient to tensor with matching dtype and device
             coeff_t = torch.as_tensor(
                 coeff, dtype=entropy_head.dtype, device=entropy_head.device
             )
+            # Compute weighted loss for this head: -coeff * entropy
             head_loss_term = -coeff_t * entropy_head
+            # Accumulate loss terms across all heads
             loss_term = (
                 head_loss_term if loss_term is None else loss_term + head_loss_term
-            )  # accumulate
+            )
 
         return loss_term
 
@@ -972,10 +1043,12 @@ class ClipPPOLoss(PPOLoss):
             ``samples_mc_entropy`` will control how many
             samples will be used to compute this estimate.
             Defaults to ``1``.
-        entropy_coeff: (scalar | Mapping[NesstedKey, scalar], optional): entropy multiplier when computing the total loss.
+        entropy_coeff: (scalar | Mapping[NestedKey, scalar], optional): entropy multiplier when computing the total loss.
             * **Scalar**: one value applied to the summed entropy of every action head.
             * **Mapping** ``{head_name: coeff}`` gives an individual coefficient for each action-head's entropy.
             Defaults to ``0.01``.
+
+            See :ref:`ppo_entropy_coefficients` for detailed usage examples and troubleshooting.
         critic_coeff (scalar, optional): critic loss multiplier when computing the total
             loss. Defaults to ``1.0``. Set ``critic_coeff`` to ``None`` to exclude the value
             loss from the forward outputs.
@@ -1269,6 +1342,8 @@ class KLPENPPOLoss(PPOLoss):
             * **Scalar**: one value applied to the summed entropy of every action head.
             * **Mapping** ``{head_name: coeff}`` gives an individual coefficient for each action-head's entropy.
             Defaults to ``0.01``.
+
+            See :ref:`ppo_entropy_coefficients` for detailed usage examples and troubleshooting.
         critic_coeff (scalar, optional): critic loss multiplier when computing the total
             loss. Defaults to ``1.0``.
         loss_critic_type (str, optional): loss function for the value discrepancy.