Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning

2025-02-26

AI Summary

Existing in-context reinforcement learning (ICRL) methods employ Transformers to imitate stepwise RL algorithms, avoiding parameter updates but inheriting suboptimality from the imitated policies. Method: We propose a model-based planning (MBP) framework that jointly distills implicit environmental dynamics modeling and in-context planning capability into a Transformer—enabling simultaneous environment modeling and policy optimization without parameter updates. Contribution/Results: Our approach breaks free from the conventional reliance on iterative update rules in imitation learning, supports both discrete and continuous control domains, and achieves state-of-the-art performance on benchmarks including Darkroom variants and Meta-World. It significantly reduces environmental interaction counts, outperforming model-agnostic meta-RL and diverse model-free baselines.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Multiagent Systems: Multiagent Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent studies have shown that Transformers can perform in-context reinforcement learning (RL) by imitating existing RL algorithms, enabling sample-efficient adaptation to unseen tasks without parameter updates. However, these models also inherit the suboptimal behaviors of the RL algorithms they imitate. This issue primarily arises due to the gradual update rule employed by those algorithms. Model-based planning offers a promising solution to this limitation by allowing the models to simulate potential outcomes before taking action, providing an additional mechanism to deviate from the suboptimal behavior. Rather than learning a separate dynamics model, we propose Distillation for In-Context Planning (DICP), an in-context model-based RL framework where Transformers simultaneously learn environment dynamics and improve policy in-context. We evaluate DICP across a range of discrete and continuous environments, including Darkroom variants and Meta-World. Our results show that DICP achieves state-of-the-art performance while requiring significantly fewer environment interactions than baselines, which include both model-free counterparts and existing meta-RL methods.

Problem

• Improves in-context reinforcement learning efficiency
• Reduces suboptimal behavior in RL algorithms
• Integrates model-based planning into Transformer frameworks

Innovation

• Transformers for in-context RL
• Distillation for In-Context Planning
• Simultaneous dynamics and policy learning