AI Insight
This study demonstrates that incorporating reinforcement learning (RL) objectives into offline in-context reinforcement learning frameworks significantly outperforms traditional supervised learning approaches. Testing across more than 150 GridWorld and MuJoCo datasets showed approximately 30% average performance improvement over the standard Algorithm Distillation method, with even greater gains (double the performance) in complex XLand-MiniGrid environments. The researchers found that adding conservatism during value learning further enhanced results across nearly all tested conditions.
Why it matters
This research provides a pathway to more efficient training of AI agents that can adapt to new tasks by learning from offline datasets, without requiring additional environment interactions. The findings could improve the development of more sample-efficient and generalizable RL systems across robotics, autonomous systems, and other domains where real-world data collection is expensive or risky.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known to have limitations in offline RL settings. In this study, we explore the integration of RL objectives within an offline ICRL framework. Through experiments on more than 150 GridWorld and MuJoCo environment-derived datasets, we demonstrate that optimizing RL objectives directly improves performance by approximately 30% on average compared to widely adopted Algorithm Distillation (AD), across various dataset coverages, structures, expertise levels, and environmental complexities. Furthermore, in the challenging XLand-MiniGrid environment, RL objectives doubled the performance of AD. Our results also reveal that the addition of conservatism during value learning brings additional improvements in almost all settings tested. Our findings emphasize the importance of aligning ICRL learning objectives with the RL reward-maximization goal, and demonstrate that offline RL is a promising direction for advancing ICRL.