Effective Reinforcement Learning Control using Conservative Soft Actor-Critic (2505.03356v1)

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

The paper introduces the Conservative Soft Actor-Critic (CSAC) algorithm, a novel approach in reinforcement learning (RL) that aims to enhance both stability and sample efficiency in complex control tasks. The CSAC is set within the Actor-Critic (AC) framework and features regularization techniques combining both entropy and relative entropy. By maintaining a balanced approach between exploration and policy stability, CSAC establishes itself as a promising alternative to existing RL methods such as Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO).

The CSAC algorithm leverages entropy regularization to foster exploration, while relative entropy constraints curb aggressive policy updates, leading to more stable learning dynamics. This approach addresses the inherent challenges of traditional RL methods in achieving optimal trade-offs between exploration and exploitation, crucially enhancing sample efficiency and learning stability.

The core innovation of CSAC involves integrating mixed entropy within both the value function and policy update mechanism, which are realized through the use of dual critic networks to mitigate overestimation biases—similarly to Twin Delayed Deep Deterministic Policy Gradient (TD3). Moreover, CSAC adapts dynamics from Conservative Value Iteration (CVI) and Dynamic Policy Programming (DPP) for use in continuous action spaces, successfully overcoming computational complexity limitations inherent to discrete action methods.

Extensive experimental evaluation across benchmark tasks such as HalfCheetah-v4, Walker2d-v4, Ant-v4, and Hopper-v4 within the MuJoCo physics engine demonstrates CSAC's robust performance. Notably, CSAC achieves higher average returns, improved convergence speeds, and superior sample efficiency compared to SAC, PPO, TD3, and SD3. Importantly, CSAC consistently exhibits strong robustness in dynamic environments, exemplified in experiments involving simulated robotic platforms—specifically UAV and robotic arm control tasks. These simulations highlight CSAC's adaptability and reliability under varying environmental conditions, positioning it as a viable candidate for real-world applications in robotics and automation.

The paper indicates promising avenues for future research, including adaptive strategies for dynamic parameter tuning and extensions to large-scale real-world environments with stringent real-time constraints. Through these developments, CSAC could further optimize RL deployment across diverse application domains, reinforcing its relevance in advancing intelligent control systems. As RL continues to mature, methodologies like CSAC represent vital steps toward more effective, efficient, and stable learning paradigms.