Value-Guided Repulsion for Offline Reinforcement Learning
To address this limitation, we introduce Value-Guided Repulsion Q-Learning (VRQL) to integrate action value estimates into the particle interaction field.
ProofPaper ↗
Key points
- Drifting models have emerged as a highly efficient and stable paradigm for policy design in offline reinforcement learning (RL).
- This mechanism assigns greater repulsive influence to particles with lower values while strictly retaining attraction toward behavior action.
- Consequently, this value-guided interaction field aligns behavior regularization with policy improvement without adding an auxiliary behavior model stage.
- On 50 OGBench task configurations and 6 D4RL AntMaze configurations, VRQL demonstrates highly competitive performance against established baselines, suggesting that value-guided particle interaction is an effective method for offline RL.
Sources (1)
- [1]Value-Guided Repulsion for Offline Reinforcement LearningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 01:39 PM
To address this limitation, we introduce Value-Guided Repulsion Q-Learning (VRQL) to integrate action value estimates into the particle interaction field.
Drifting models have emerged as a highly efficient and stable paradigm for policy design in offline reinforcement learning (RL).
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