chore: import upstream snapshot with attribution

rllib/algorithms/iql/default_iql_rl_module.py

@@ -0,0 +1,35 @@
+from ray.rllib.algorithms.sac.default_sac_rl_module import DefaultSACRLModule
+from ray.rllib.core.models.configs import MLPHeadConfig
+from ray.rllib.core.rl_module.apis.value_function_api import ValueFunctionAPI
+from ray.rllib.utils.annotations import (
+    OverrideToImplementCustomLogic_CallToSuperRecommended,
+    override,
+)
+
+
+class DefaultIQLRLModule(DefaultSACRLModule, ValueFunctionAPI):
+    @override(DefaultSACRLModule)
+    def setup(self):
+        # Setup the `DefaultSACRLModule` to get the catalog.
+        super().setup()
+
+        # Only, if the `RLModule` is used on a `Learner` we build the value network.
+        if not self.inference_only:
+            # Build the encoder for the value function.
+            self.vf_encoder = self.catalog.build_encoder(framework=self.framework)
+
+            # Build the vf head.
+            self.vf = MLPHeadConfig(
+                input_dims=self.catalog.latent_dims,
+                # Note, we use the same layers as for the policy and Q-network.
+                hidden_layer_dims=self.catalog.pi_and_qf_head_hiddens,
+                hidden_layer_activation=self.catalog.pi_and_qf_head_activation,
+                output_layer_activation="linear",
+                output_layer_dim=1,
+            ).build(framework=self.framework)
+
+    @override(DefaultSACRLModule)
+    @OverrideToImplementCustomLogic_CallToSuperRecommended
+    def get_non_inference_attributes(self):
+        # Use all of `super`'s attributes and add the value function attributes.
+        return super().get_non_inference_attributes() + ["vf_encoder", "vf"]