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

Hao Ma, Zhiqiang Pu, Xiaolin Ai, Huimu Wang

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.

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

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.
Paper Structure (24 sections, 7 theorems, 21 equations, 8 figures, 1 table, 1 algorithm)

This paper contains 24 sections, 7 theorems, 21 equations, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminary
  3. Markov Decision Process
  4. Maximum Entropy RL and SAC
  5. Method
  6. Derivation of Guided Soft Actor-Critic
  7. Guided Soft Actor-Critic
  8. LLM-based Supervisor
  9. Experiment
  10. Discrete Control Tasks
  11. Continuous Control Tasks
  12. Ablation Study
  13. Related Work
  14. Conclusion
  15. Proof of Convergence
  16. ...and 9 more sections

Key Result

Lemma 1

Consider the guided Bellman backup operator $\mathcal{T}^{\widetilde{\pi}}$ and a mapping $Q:\mathcal{S}\times\mathcal{A}\rightarrow\mathbb{R}$, and define $Q^{k+1}=\mathcal{T}^{\widetilde{\pi}} Q^k$. Then the sequence $Q^k$ will converge to the Q-value of $\widetilde{\pi}$ as $k\rightarrow\infty$.

Figures (8)

  • Figure 1: The framework of GuidedSAC. GuidedSAC leverages an LLM-based supervisor to analyze the last trajectory and determine whether intervention is necessary. If intervention is triggered, a residual action $\Delta a$ is added to the original action $a$, resulting in the intervened action $\widetilde{a}$. This intervened action is then stored in the replay buffer, facilitating the discovery of high-value trajectories.
  • Figure 2: Illustration of the LLM-based supervisor's cooperation details.
  • Figure 3: Performance comparison on toy text. Training curves comparing GuidedSAC with RND, E3B, and ICM across four toy text environments. The shaded regions represent the predefined intervals where intervention can occur.
  • Figure 4: Training curves for MountainCar and Humanoid. Shaded regions indicate intervention periods for GuidedSAC. For MountainCar the intervention occurs between steps 50k and 53k while for Humanoid it occurs between steps 700k and 800k. MountainCar reward of 100 indicates successful goal achievement. Humanoid values above 5000 represent robust bipedal locomotion.
  • Figure 5: Policy landscape evolution in MountainCar. Columns show snapshots at 0k, 50k intervention start, 53k intervention end, 60k, 80k, and 100k training steps. Horizontal axis shows car position with $x \in [-1.3, 0.7]$. Vertical axis shows velocity with $v \in [-0.07, 0.07]$. Color indicates most probable action $\arg\max_a \pi(a|s)$. Dotted lines intersect at initial state with $x=-0.5$ and $v=0$. The intervention window causes immediate policy reconfiguration, demonstrating efficient knowledge transfer from $\pi_{\text{interv}}$ to $\pi_\phi$.
  • ...and 3 more figures

Theorems & Definitions (11)

  • Lemma 1: Guided Policy Evaluation
  • Lemma 2: Guided Policy Improvement
  • Theorem 1: Convergence of GuidedSAC
  • Proposition 1: Single Step Improvement
  • proof
  • Lemma 3: Guided Policy Evaluation
  • proof
  • Lemma 4: Guided Policy Improvement
  • proof
  • Theorem 2: Convergence of GuidedSAC
  • ...and 1 more