基于动态拓扑的非线性强化学习算法

Abstract

This paper introduces a novel dynamic topology-based non-linear reinforcement learning (RL) algorithm designed to enhance learning efficiency and robustness. Traditional reinforcement learning methods often rely on static strategies, limiting adaptability and vulnerability to environmental changes. Our algorithm dynamically adjusts the topology of the state space, effectively simulating the learning process and mitigating the impact of perturbations. We explore how this dynamic structure contributes to improved performance across a range of tasks. The core mechanism centers around the continuous evolution of state representations, driven by a simulated topology, enabling the agent to better generalize to unseen scenarios. This work presents a framework for building more robust and adaptable reinforcement learning agents.