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Effective Reinforcement Learning Control using Conservative Soft Actor-Critic

Xinyi Yuan, Zhiwei Shang, Wenjun Huang, Yunduan Cui, Di Chen, Meixin Zhu

TL;DR

CSAC proposes a unified reinforcement learning algorithm that merges entropy and relative-entropy regularization inside an Actor-Critic framework to improve exploration, stabilize updates, and boost sample efficiency for continuous control. It extends the value function with dual regularization terms and uses a conservative policy update derived from an energy-based perspective, implemented with two critics to reduce overestimation. Empirical results across MuJoCo benchmarks and real-robot-based simulations show CSAC consistently achieves higher final performance, faster convergence, and greater robustness than SAC, PPO, TD3, and SD3, particularly in high-dimensional and dynamic environments. The work demonstrates practical potential for CSAC in real-world robotics, and suggests adaptive, task-aware tuning of regularization parameters as a promising direction for future deployment.

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.

Effective Reinforcement Learning Control using Conservative Soft Actor-Critic

TL;DR

CSAC proposes a unified reinforcement learning algorithm that merges entropy and relative-entropy regularization inside an Actor-Critic framework to improve exploration, stabilize updates, and boost sample efficiency for continuous control. It extends the value function with dual regularization terms and uses a conservative policy update derived from an energy-based perspective, implemented with two critics to reduce overestimation. Empirical results across MuJoCo benchmarks and real-robot-based simulations show CSAC consistently achieves higher final performance, faster convergence, and greater robustness than SAC, PPO, TD3, and SD3, particularly in high-dimensional and dynamic environments. The work demonstrates practical potential for CSAC in real-world robotics, and suggests adaptive, task-aware tuning of regularization parameters as a promising direction for future deployment.

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.
Paper Structure (20 sections, 24 equations, 9 figures, 9 tables, 1 algorithm)

This paper contains 20 sections, 24 equations, 9 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Markov Decision Processes
  4. Actor-Critic Framework
  5. Approach
  6. Mix Entropy and Relative Entropy in Value Function
  7. Policy Improvement with Mixed Entropy and Relative Entropy Regularization
  8. Summary of Conservative Soft Actor-Critic
  9. Experiments
  10. Experimental Settings
  11. Evaluation of Learning Capability on MuJoCo Tasks
  12. Ablation Study on Walker2d Task
  13. Evaluation of Sample Efficiency
  14. Evaluation of Robustness in HalfCheetah Tak
  15. Implementation on Real-Robot-Based Simulation
  16. ...and 5 more sections

Figures (9)

  • Figure 1: Benchmark tasks for evaluation in this article. (a) HalfCheetah-v4. (b) Walker2d-v4. (c) Ant-v4. (d) Hopper-v4.
  • Figure 2: Learning curves over four benchmark tasks. Curves are uniformly smoothed for visual clarity. The shaded region represents the corresponding standard deviation.
  • Figure 3: Learning curves of CSAC with different $\tau$ values in the Walker2d-v4 task. The shaded region represents the standard deviation of the average evaluation over five trials. Curves are smoothed uniformly for visual clarity.
  • Figure 4: Number of interactions used by all compared approaches to reach the lower boundary of the maximum average returns over four benchmark control tasks.
  • Figure 5: Performance comparison of CSAC and SAC under varying Friction-Induced dynamic noise in the HalfCheetah-v4 environment. The shaded region represents the corresponding standard deviation.
  • ...and 4 more figures