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