Debiased Model-based Representations for Sample-efficient Continuous Control

• URL: http://arxiv.org/abs/2605.11711v1
• Date: Tue, 12 May 2026 08:02:34 GMT
• Title: Debiased Model-based Representations for Sample-efficient Continuous Control
• Abstract summary: We propose Debiased model-based Representations for Q-learning, tagged DR.Q algorithm.<n> DR.Q explicitly maximizes the mutual information between the representations of the current state-action pair and the next state.<n>Our results show DR.Q can match or surpass recent strong baselines, sometimes outperforming them by a large margin.
• Score: 36.705558101460205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream off-policy actor-critic learning. It implicitly combines the advantages of both model-free and model-based approaches while avoiding the training costs associated with model-based methods. Nevertheless, existing model-based representation methods can fail to capture sufficient information about relevant variables and can overfit to early experiences in the replay buffer. These incur biases in representation and actor-critic learning, leading to inferior performance. To address this, we propose Debiased model-based Representations for Q-learning, tagged DR.Q algorithm. DR.Q explicitly maximizes the mutual information between the representations of the current state-action pair and the next state besides minimizing their deviations, and samples transitions with faded prioritized experience replay. We evaluate DR.Q on numerous continuous control benchmarks with a single set of hyperparameters, and the results demonstrate that DR.Q can match or surpass recent strong baselines, sometimes outperforming them by a large margin. Our code is available at https://github.com/dmksjfl/DR.Q.

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