[RLlib; Offline RL] Implement twin-Q net option for CQL.

rllib/algorithms/cql/torch/cql_torch_learner.py

@@ -9,6 +9,8 @@
     QF_MAX_KEY,
     QF_MIN_KEY,
     QF_PREDS,
+    QF_TWIN_LOSS_KEY,
+    QF_TWIN_PREDS,
     TD_ERROR_MEAN_KEY,
 )
 from ray.rllib.algorithms.cql.cql import CQLConfig
@@ -90,8 +92,9 @@ def compute_loss_for_module(
                 # Use the actions sampled from the current policy.
                 Columns.ACTIONS: actions_curr,
             }
+            # Note, if `twin_q` is `True`, `compute_q_values` computes the minimum
+            # of the `qf` and `qf_twin` and returns this minimum.
             q_curr = self.module[module_id].compute_q_values(batch_curr)
-            # TODO (simon): Add twin Q
             actor_loss = torch.mean(alpha.detach() * logps_curr - q_curr)
         else:
             # Use log-probabilities of the current action distribution to clone
@@ -117,16 +120,19 @@ def compute_loss_for_module(
         # Get the Q-values for the actually selected actions in the offline data.
         # In the critic loss we use these as predictions.
         q_selected = fwd_out[QF_PREDS]
-        # TODO (simon): Implement twin Q
+        if config.twin_q:
+            q_twin_selected = fwd_out[QF_TWIN_PREDS]
 
         # Compute Q-values from the target Q network for the next state with the
         # sampled actions for the next state.
         q_batch_next = {
             Columns.OBS: batch[Columns.NEXT_OBS],
             Columns.ACTIONS: actions_next,
         }
+        # Note, if `twin_q` is `True`, `SACTorchRLModule.forward_target` calculates
+        # the Q-values for both, `qf_target` and `qf_twin_target` and
+        # returns the minimum.
         q_target_next = self.module[module_id].forward_target(q_batch_next)
-        # TODO (simon): Apply twin Q
 
         # Now mask all Q-values with terminating next states in the targets.
         q_next_masked = (1.0 - batch[Columns.TERMINATEDS].float()) * q_target_next
@@ -143,16 +149,25 @@ def compute_loss_for_module(
 
         # Calculate the TD error.
         td_error = torch.abs(q_selected - q_selected_target)
-        # TODO (simon): Add the Twin TD error
+        # Calculate a TD-error for twin-Q values, if needed.
+        if config.twin_q:
+            td_error += torch.abs(q_twin_selected - q_selected_target)
+            # Rescale the TD error
+            td_error *= 0.5
 
         # MSBE loss for the critic(s) (i.e. Q, see eqs. (7-8) Haarnoja et al. (2018)).
         # Note, this needs a sample from the current policy given the next state.
         # Note further, we could also use here the Huber loss instead of the MSE.
         # TODO (simon): Add the huber loss as an alternative (SAC uses it).
         sac_critic_loss = torch.nn.MSELoss(reduction="mean")(
-            q_selected, q_selected_target
+            q_selected,
+            q_selected_target,
         )
-        # TODO (simon): Add the Twin Q critic loss
+        if config.twin_q:
+            sac_critic_twin_loss = torch.nn.MSELoss(reduction="mean")(
+                q_twin_selected,
+                q_selected_target,
+            )
 
         # Now calculate the CQL loss (we use the entropy version of the CQL algorithm).
         # Note, the entropy version performs best in shown experiments.
@@ -185,34 +200,88 @@ def compute_loss_for_module(
             Columns.OBS: obs_curr_repeat,
             Columns.ACTIONS: actions_rand_repeat,
         }
+        # Note, we need here the Q-values from the base Q-value function
+        # and not the minimum with an eventual Q-value twin.
         q_rand_repeat = (
             self.module[module_id]
-            .compute_q_values(batch_rand_repeat)
+            ._qf_forward_train_helper(
+                batch_rand_repeat,
+                self.module[module_id].qf_encoder,
+                self.module[module_id].qf,
+            )
             .view(batch_size, config.num_actions, 1)
         )
+        # Calculate twin Q-values for the random actions, if needed.
+        if config.twin_q:
+            q_twin_rand_repeat = (
+                self.module[module_id]
+                ._qf_forward_train_helper(
+                    batch_rand_repeat,
+                    self.module[module_id].qf_twin_encoder,
+                    self.module[module_id].qf_twin,
+                )
+                .view(batch_size, config.num_actions, 1)
+            )
         del batch_rand_repeat
         batch_curr_repeat = {
             Columns.OBS: obs_curr_repeat,
             Columns.ACTIONS: actions_curr_repeat,
         }
         q_curr_repeat = (
             self.module[module_id]
-            .compute_q_values(batch_curr_repeat)
+            ._qf_forward_train_helper(
+                batch_curr_repeat,
+                self.module[module_id].qf_encoder,
+                self.module[module_id].qf,
+            )
             .view(batch_size, config.num_actions, 1)
         )
+        # Calculate twin Q-values for the repeated actions from the current policy,
+        # if needed.
+        if config.twin_q:
+            q_twin_curr_repeat = (
+                self.module[module_id]
+                ._qf_forward_train_helper(
+                    batch_curr_repeat,
+                    self.module[module_id].qf_twin_encoder,
+                    self.module[module_id].qf_twin,
+                )
+                .view(batch_size, config.num_actions, 1)
+            )
         del batch_curr_repeat
         batch_next_repeat = {
+            # Note, we use here the current observations b/c we want to keep the
+            # state fix while sampling the actions.
             Columns.OBS: obs_curr_repeat,
             Columns.ACTIONS: actions_next_repeat,
         }
         q_next_repeat = (
             self.module[module_id]
-            .compute_q_values(batch_next_repeat)
+            ._qf_forward_train_helper(
+                batch_next_repeat,
+                self.module[module_id].qf_encoder,
+                self.module[module_id].qf,
+            )
             .view(batch_size, config.num_actions, 1)
         )
+        # Calculate also the twin Q-values for the current policy and next actions,
+        # if needed.
+        if config.twin_q:
+            q_twin_next_repeat = (
+                self.module[module_id]
+                ._qf_forward_train_helper(
+                    batch_next_repeat,
+                    self.module[module_id].qf_twin_encoder,
+                    self.module[module_id].qf_twin,
+                )
+                .view(batch_size, config.num_actions, 1)
+            )
         del batch_next_repeat
 
         # Compute the log-probabilities for the random actions.
+        # TODO (simon): This is the density for a discrete uniform, however, actions
+        # come from a continuous one. So actually this density should use (1/(high-low))
+        # instead of (1/2).
         random_density = torch.log(
             torch.pow(
                 torch.tensor(
@@ -231,23 +300,43 @@ def compute_loss_for_module(
             ],
             dim=1,
         )
-
         cql_loss = (
             torch.logsumexp(q_repeat / config.temperature, dim=1).mean()
             * config.min_q_weight
             * config.temperature
         )
-        cql_loss = cql_loss - (q_selected.mean() * config.min_q_weight)
-        # TODO (simon): Implement CQL twin-Q loss here
+        cql_loss -= q_selected.mean() * config.min_q_weight
+        # Add the CQL loss term to the SAC loss term.
+        critic_loss = sac_critic_loss + cql_loss
+
+        # If a twin Q-value function is implemented calculated its CQL loss.
+        if config.twin_q:
+            q_twin_repeat = torch.cat(
+                [
+                    q_twin_rand_repeat - random_density,
+                    q_twin_next_repeat - logps_next_repeat.detach(),
+                    q_twin_curr_repeat - logps_curr_repeat.detach(),
+                ],
+                dim=1,
+            )
+            cql_twin_loss = (
+                torch.logsumexp(q_twin_repeat / config.temperature, dim=1).mean()
+                * config.min_q_weight
+                * config.temperature
+            )
+            cql_twin_loss -= q_twin_selected.mean() * config.min_q_weight
+            # Add the CQL loss term to the SAC loss term.
+            critic_twin_loss = sac_critic_twin_loss + cql_twin_loss
 
         # TODO (simon): Check, if we need to implement here also a Lagrangian
         # loss.
 
-        critic_loss = sac_critic_loss + cql_loss
-        # TODO (simon): Add here also the critic loss for the twin-Q
-
         total_loss = actor_loss + critic_loss + alpha_loss
-        # TODO (simon): Add Twin Q losses
+
+        # Add the twin critic loss to the total loss, if needed.
+        if config.twin_q:
+            # Reweigh the critic loss terms in the total loss.
+            total_loss += 0.5 * critic_twin_loss - 0.5 * critic_loss
 
         # Log important loss stats (reduce=mean (default), but with window=1
         # in order to keep them history free).
@@ -273,7 +362,14 @@ def compute_loss_for_module(
         )
         # TODO (simon): Add loss keys for langrangian, if needed.
         # TODO (simon): Add only here then the Langrange parameter optimization.
-        # TODO (simon): Add keys for twin Q
+        if config.twin_q:
+            self.metrics.log_dict(
+                {
+                    QF_TWIN_LOSS_KEY: critic_twin_loss,
+                },
+                key=module_id,
+                window=1,  # <- single items (should not be mean/ema-reduced over time).
+            )
 
         # Return the total loss.
         return total_loss

rllib/algorithms/sac/torch/sac_torch_learner.py

@@ -232,6 +232,7 @@ def compute_loss_for_module(
         total_loss = actor_loss + critic_loss + alpha_loss
         # If twin Q networks should be used, add the critic loss of the twin Q network.
         if config.twin_q:
+            # TODO (simon): Check, if we need to multiply the critic_loss then with 0.5.
             total_loss += critic_twin_loss
 
         # Log the TD-error with reduce=None, such that - in case we have n parallel