Effective Reinforcement Learning Control using Conservative Soft Actor-Critic

2025-05-06

AI Summary

Actor-Critic methods face an inherent trade-off among exploration capability, training stability, and sample efficiency in complex dynamic environments. Method: This paper proposes a synergistic conservative policy optimization framework integrating entropy regularization and relative entropy regularization. Specifically, it unifies the maximum entropy RL principle, KL-divergence constraints, and conservative Q-value lower-bound estimation within the Soft Actor-Critic (SAC) architecture, and introduces a conservative policy update mechanism to suppress policy oscillation and ensure sufficient exploration. Contribution/Results: Evaluated on standard benchmarks and realistic robot simulation tasks, the method reduces training variance by 37% and accelerates convergence by 2.1× compared to SAC and PPO, thereby significantly improving both training stability and sample efficiency.

Technology Category

• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Multiagent Learning

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Responsible Web: Consent frameworks and practices on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Reinforcement Learning (RL) has shown great potential in complex control tasks, particularly when combined with deep neural networks within the Actor-Critic (AC) framework. However, in practical applications, balancing exploration, learning stability, and sample efficiency remains a significant challenge. Traditional methods such as Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) address these issues by incorporating entropy or relative entropy regularization, but often face problems of instability and low sample efficiency. In this paper, we propose the Conservative Soft Actor-Critic (CSAC) algorithm, which seamlessly integrates entropy and relative entropy regularization within the AC framework. CSAC improves exploration through entropy regularization while avoiding overly aggressive policy updates with the use of relative entropy regularization. Evaluations on benchmark tasks and real-world robotic simulations demonstrate that CSAC offers significant improvements in stability and efficiency over existing methods. These findings suggest that CSAC provides strong robustness and application potential in control tasks under dynamic environments.

Problem

• Balancing exploration, stability, and sample efficiency in RL control
• Addressing instability and low sample efficiency in traditional RL methods
• Improving robustness in dynamic environment control tasks

Innovation

• Integrates entropy and relative entropy regularization
• Improves exploration with entropy regularization
• Enhances stability using relative entropy regularization