Academic

Efficient Soft Actor-Critic with LLM-Based Action-Level Guidance for Continuous Control

arXiv:2603.17468v1 Announce Type: new Abstract: We present GuidedSAC, a novel reinforcement learning (RL) algorithm that facilitates efficient exploration in vast state-action spaces. GuidedSAC leverages large language models (LLMs) as intelligent supervisors that provide action-level guidance for the Soft Actor-Critic (SAC) algorithm. The LLM-based supervisor analyzes the most recent trajectory using state information and visual replays, offering action-level interventions that enable targeted exploration. Furthermore, we provide a theoretical analysis of GuidedSAC, proving that it preserves the convergence guarantees of SAC while improving convergence speed. Through experiments in both discrete and continuous control environments, including toy text tasks and complex MuJoCo benchmarks, we demonstrate that GuidedSAC consistently outperforms standard SAC and state-of-the-art exploration-enhanced variants (e.g., RND, ICM, and E3B) in terms of sample efficiency and final performance.

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Hao Ma, Zhiqiang Pu, Xiaolin Ai, Huimu Wang · · 1 min read · 3 views
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arXiv:2603.17468v1 Announce Type: new Abstract: We present GuidedSAC, a novel reinforcement learning (RL) algorithm that facilitates efficient exploration in vast state-action spaces. GuidedSAC leverages large language models (LLMs) as intelligent supervisors that provide action-level guidance for the Soft Actor-Critic (SAC) algorithm. The LLM-based supervisor analyzes the most recent trajectory using state information and visual replays, offering action-level interventions that enable targeted exploration. Furthermore, we provide a theoretical analysis of GuidedSAC, proving that it preserves the convergence guarantees of SAC while improving convergence speed. Through experiments in both discrete and continuous control environments, including toy text tasks and complex MuJoCo benchmarks, we demonstrate that GuidedSAC consistently outperforms standard SAC and state-of-the-art exploration-enhanced variants (e.g., RND, ICM, and E3B) in terms of sample efficiency and final performance.

Executive Summary

This study presents GuidedSAC, a novel reinforcement learning algorithm that integrates large language models (LLMs) with Soft Actor-Critic (SAC) to facilitate efficient exploration in vast state-action spaces. GuidedSAC leverages LLM-based supervisors to provide action-level guidance, enabling targeted exploration and outperforming standard SAC and state-of-the-art exploration-enhanced variants in both discrete and continuous control environments. Theoretical analysis proves that GuidedSAC preserves SAC's convergence guarantees while improving convergence speed. Through experiments, GuidedSAC demonstrates significant improvements in sample efficiency and final performance, solidifying its potential as a superior RL algorithm.

Key Points

  • ▸ GuidedSAC integrates LLMs with SAC for efficient exploration
  • ▸ LLM-based supervisors provide action-level guidance for targeted exploration
  • ▸ Theoretical analysis proves convergence guarantees and improved convergence speed

Merits

Strength

GuidedSAC demonstrates superior performance in both discrete and continuous control environments, outperforming standard SAC and state-of-the-art exploration-enhanced variants.

Improved Convergence

Theoretical analysis shows that GuidedSAC preserves SAC's convergence guarantees while significantly improving convergence speed.

Demerits

Limitation

GuidedSAC relies on the quality and availability of LLMs, which may not always be feasible or reliable in real-world scenarios.

Dependence on Trajectory Analysis

The LLM-based supervisors rely on analyzing recent trajectories, which may not be suitable for real-time applications or situations with rapidly changing environments.

Expert Commentary

While GuidedSAC demonstrates impressive performance and efficiency, its reliance on LLMs and trajectory analysis raises concerns about its feasibility and adaptability in real-world scenarios. Further research is needed to address these limitations and explore the potential of GuidedSAC in more complex and dynamic environments. Moreover, the integration of LLMs and SAC raises important questions about the role of AI and machine learning in policy-making and decision-making processes, and the need for transparent and explainable decision-making.

Recommendations

  • ✓ Further research is needed to address the limitations of GuidedSAC and explore its potential in real-world scenarios.
  • ✓ The development of more robust and reliable LLMs is essential for the widespread adoption of GuidedSAC and similar algorithms.

Sources

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