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[2601.19720] Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Action
Summary
The paper presents a novel algorithm, Instant Retrospect Action (IRA), aimed at enhancing policy exploitation in online reinforcement learning by improving exploration and policy update efficiency.
Why It Matters
As reinforcement learning continues to evolve, improving the efficiency of policy exploitation is crucial for developing more effective AI systems. This research addresses key challenges in value-based online RL, potentially leading to advancements in various applications, including robotics and autonomous systems.
Key Takeaways
- Introduces Instant Retrospect Action (IRA) to enhance policy exploitation.
- Proposes Q-Representation Discrepancy Evolution (RDE) for better Q-network learning.
- Implements Greedy Action Guidance (GAG) for improved policy constraints.
- Enhances policy update frequency with the Instant Policy Update (IPU) mechanism.
- Demonstrates significant improvements in learning efficiency on MuJoCo continuous control tasks.
Computer Science > Machine Learning arXiv:2601.19720 (cs) [Submitted on 27 Jan 2026 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Action Authors:Gong Gao, Weidong Zhao, Xianhui Liu, Ning Jia View a PDF of the paper titled Improving Policy Exploitation in Online Reinforcement Learning with Instant Retrospect Action, by Gong Gao and 3 other authors View PDF HTML (experimental) 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 polic...
