Improving policy exploitation in online reinforcement learning with instant retrospect action.

2026-01-27

Neural Networks

AI Summary

This work addresses the slow learning often observed in existing value-based online reinforcement learning algorithms, which stems from inefficient exploration and delayed policy updates. To overcome these limitations, the paper introduces three key techniques: Representation Difference Evolution (RDE) to enhance the discriminative power of state-action representations, Greedy Action Guidance (GAG) to improve the directionality of exploration, and Instant Policy Update (IPU) to eliminate policy lag. Additionally, the authors incorporate k-nearest neighbor action-value estimation and demonstrate that adopting a conservative policy during early training effectively mitigates value overestimation. Evaluated on eight MuJoCo continuous control tasks, the proposed method achieves substantial improvements in both sample efficiency and final performance.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Social networks and social learning

Problem

• policy exploitation
• online reinforcement learning
• exploration inefficiency
• delayed policy updates
• value-based RL

Innovation

• Instant Retrospect Action
• Q-Representation Discrepancy Evolution
• Greedy Action Guidance
• Instant Policy Update
• Policy Constraints