wehub-resource-sync
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
"""
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TQC Learner base class.
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Extends SAC Learner with quantile-specific loss computation.
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"""
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from ray.rllib.algorithms.sac.sac_learner import SACLearner
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from ray.rllib.core.learner.learner import Learner
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.typing import ModuleID
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# Loss keys for TQC
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QF_LOSS_KEY = "qf_loss"
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QF_MEAN_KEY = "qf_mean"
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QF_MAX_KEY = "qf_max"
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QF_MIN_KEY = "qf_min"
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QUANTILES_KEY = "quantiles"
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TD_ERROR_MEAN_KEY = "td_error_mean"
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class TQCLearner(SACLearner):
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"""Base Learner class for TQC algorithm.
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TQC extends SAC with distributional critics using quantile regression.
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The main differences are:
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- Uses quantile Huber loss instead of standard Huber/MSE loss
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- Computes target Q-values by sorting and truncating top quantiles
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"""
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@override(Learner)
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def build(self) -> None:
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"""Builds the TQC learner."""
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# Call parent build (handles alpha/entropy coefficient)
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super().build()
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def _get_n_target_quantiles(self, module_id: ModuleID) -> int:
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"""Returns the number of target quantiles after truncation.
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Args:
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module_id: The module ID.
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Returns:
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Number of quantiles to use for target computation.
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"""
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config = self.config.get_config_for_module(module_id)
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n_quantiles = config.n_quantiles
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n_critics = config.n_critics
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top_quantiles_to_drop = config.top_quantiles_to_drop_per_net
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total_quantiles = n_quantiles * n_critics
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quantiles_to_drop = top_quantiles_to_drop * n_critics
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return total_quantiles - quantiles_to_drop
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