Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Action

• URL: http://arxiv.org/abs/2601.19720v1
• Date: Tue, 27 Jan 2026 15:43:02 GMT
• Title: Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Action
• Authors: Gong Gao, Weidong Zhao, Xianhui Liu, Ning Jia,
• Abstract summary: Existing value-based online reinforcement learning (RL) algorithms suffer from slow policy exploitation due to ineffective exploration and delayed policy updates.<n>We propose an algorithm called Instant Retrospect Action (IRA) to address these challenges.<n>IRA can significantly improve the learning efficiency and final performance of online RL algorithms on eight MuJoCo continuous control tasks.
• Score: 10.983482150597913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing value-based online reinforcement learning (RL) algorithms suffer from slow policy exploitation due to ineffective exploration and delayed policy updates. To address these challenges, we propose an algorithm called Instant Retrospect Action (IRA). Specifically, we propose Q-Representation Discrepancy Evolution (RDE) to facilitate Q-network representation learning, enabling discriminative representations for neighboring state-action pairs. In addition, we adopt an explicit method to policy constraints by enabling Greedy Action Guidance (GAG). This is achieved through backtracking historical actions, which effectively enhances the policy update process. Our proposed method relies on providing the learning algorithm with accurate $k$-nearest-neighbor action value estimates and learning to design a fast-adaptable policy through policy constraints. We further propose the Instant Policy Update (IPU) mechanism, which enhances policy exploitation by systematically increasing the frequency of policy updates. We further discover that the early-stage training conservatism of the IRA method can alleviate the overestimation bias problem in value-based RL. Experimental results show that IRA can significantly improve the learning efficiency and final performance of online RL algorithms on eight MuJoCo continuous control tasks.

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