Mildly Conservative Regularized Evaluation for Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2508.05960v1
• Date: Fri, 08 Aug 2025 02:48:26 GMT
• Title: Mildly Conservative Regularized Evaluation for Offline Reinforcement Learning
• Authors: Haohui Chen, Zhiyong Chen,
• Abstract summary: offline reinforcement learning seeks to learn optimal policies from static datasets without further environment interaction.<n>To prevent gross overestimation, the value function must remain conservative.<n>We propose the mildly conservative regularized evaluation (MCRE) framework, which balances conservatism and performance.
• Score: 4.657497798824256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline reinforcement learning (RL) seeks to learn optimal policies from static datasets without further environment interaction. A key challenge is the distribution shift between the learned and behavior policies, leading to out-of-distribution (OOD) actions and overestimation. To prevent gross overestimation, the value function must remain conservative; however, excessive conservatism may hinder performance improvement. To address this, we propose the mildly conservative regularized evaluation (MCRE) framework, which balances conservatism and performance by combining temporal difference (TD) error with a behavior cloning term in the Bellman backup. Building on this, we develop the mildly conservative regularized Q-learning (MCRQ) algorithm, which integrates MCRE into an off-policy actor-critic framework. Experiments show that MCRQ outperforms strong baselines and state-of-the-art offline RL algorithms on benchmark datasets.

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