A Policy Optimization Method Towards Optimal-time Stability

TL;DR

This paper introduces ALAC, a novel reinforcement learning algorithm that ensures systems reach stable equilibrium within optimal time using Lyapunov stability, improving performance on robotic tasks.

Contribution

It proposes a new policy optimization method incorporating sampling-based Lyapunov stability to achieve optimal-time stability in RL.

Findings

ALAC outperforms previous methods on robotic tasks.

It effectively guides systems to stable equilibrium within optimal time.

The approach enhances stability and performance in model-free RL.

Abstract

In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization. However, these criteria only guarantee the infinite-time convergence of the system's state to an equilibrium point, which leads to sub-optimality of the policy. In this paper, we propose a policy optimization technique incorporating sampling-based Lyapunov stability. Our approach enables the system's state to reach an equilibrium point within an optimal time and maintain stability thereafter, referred to as "optimal-time stability". To achieve this, we integrate the optimization method into the Actor-Critic framework, resulting in the development of the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm. Through evaluations conducted on ten robotic tasks, our approach outperforms previous studies significantly, effectively guiding the system to generate stable patterns.