DR.Q debiases model-based representations for Q-learning by maximizing mutual information between state-action and next-state representations and applying faded prioritized experience replay, achieving competitive or superior performance on continuous control benchmarks.
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Debiased Model-based Representations for Sample-efficient Continuous Control
DR.Q debiases model-based representations for Q-learning by maximizing mutual information between state-action and next-state representations and applying faded prioritized experience replay, achieving competitive or superior performance on continuous control benchmarks.