In-Context Policy Iteration: Enhancing Reinforcement Learning with Large Language Models

The paper introduces In-Context Policy Iteration (ICPI) as a novel approach that leverages large language models (LLMs) for reinforcement learning (RL) tasks. ICPI eliminates the need for expert demonstrations and computationally intensive gradient methods by utilizing in-context learning from prompts to iteratively update the LLM’s content based on interactions with the environment.
Reinforcement Learning
Large Language Models
AI
Policy Iteration
Published

August 14, 2024

Engineers and specialists can benefit from the paper’s insights by understanding how ICPI outperforms traditional RL methods through prompt-based learning, the role of rollout policy and world model in guiding the LLM’s decision-making, and the impact of model size on ICPI’s performance in handling complex RL tasks.

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