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LLM-Empowered State Representation for Reinforcement Learning

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arxiv 2407.13237 v1 pith:RXV4V2U7 submitted 2024-07-18 cs.AI

Boyuan Wang , Yun Qu , Yuhang Jiang , Jianzhun Shao , Chang Liu , Wenming Yang , Xiangyang Ji This is my paper · ORCID
classification cs.AI
keywords statelearningrepresentationsampleefficiencyhighlesrllm-empowered
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Conventional state representations in reinforcement learning often omit critical task-related details, presenting a significant challenge for value networks in establishing accurate mappings from states to task rewards. Traditional methods typically depend on extensive sample learning to enrich state representations with task-specific information, which leads to low sample efficiency and high time costs. Recently, surging knowledgeable large language models (LLM) have provided promising substitutes for prior injection with minimal human intervention. Motivated by this, we propose LLM-Empowered State Representation (LESR), a novel approach that utilizes LLM to autonomously generate task-related state representation codes which help to enhance the continuity of network mappings and facilitate efficient training. Experimental results demonstrate LESR exhibits high sample efficiency and outperforms state-of-the-art baselines by an average of 29% in accumulated reward in Mujoco tasks and 30% in success rates in Gym-Robotics tasks.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MARCO: Meta-Reflection with Cross-Referencing for Code Reasoning

    cs.CL 2025-05 conditional novelty 6.0 of 10

    MARCO combines cross-problem knowledge accumulation with cross-agent lesson sharing to improve LLM code reasoning at inference time.

  2. Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One

    cs.LG 2025-05 reject novelty 6.0 of 10

    An LLM classifies game states into situations and selects the RL agent with the best historical average reward for each situation, outperforming static ensemble baselines on Atari.

  3. ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing

    cs.LG 2025-05 reject novelty 4.0 of 10

    ORAN-GUIDE couples a domain-specific LLM prompt generator with a frozen GPT-2 encoder and learnable prompt tokens to improve multi-agent SAC sample efficiency in O-RAN slicing.

  4. Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing

    cs.LG 2025-05 conditional novelty 4.0 of 10

    Prompt-tuned ORANSight state representations improve convergence and slice-level QoS for multi-agent SAC in a simulated O-RAN slicing environment, according to the reported ablation.

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