Q-learning integration boosts offline In-Context RL performance

A new research paper explores the effectiveness of integrating Reinforcement Learning (RL) objectives into offline In-Context Reinforcement Learning (ICRL) methods. Experiments across over 150 datasets in GridWorld and MuJoCo environments showed that directly optimizing RL objectives improved performance by approximately 30% on average compared to standard Algorithm Distillation (AD). In the XLand-MiniGrid environment, RL objectives doubled AD's performance, and adding conservatism during value learning further enhanced results in most tested scenarios. The findings highlight the importance of aligning ICRL learning objectives with RL's reward-maximization goal. AI

IMPACT This research suggests that aligning ICRL learning objectives with RL reward-maximization goals can significantly improve performance, potentially leading to more effective offline AI agents.

RANK_REASON This is a research paper published on arXiv detailing new findings in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Algorithm Distillation
• Denis Tarasov
• GridWorld
• MuJoCo
• offline In-Context RL
• Q-learning
• XLand-MiniGrid

• paper
• other

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