基于拓扑网络的自适应强化学习算法

Abstract

This paper introduces a novel self-adaptive reinforcement learning algorithm based on topological networks. Traditional reinforcement learning approaches often rely on predefined reward functions and static policies, limiting their ability to adapt to dynamic environments. Our algorithm leverages the inherent topological structure of the environment to enable more intelligent and adaptive decision-making. We propose a method for constructing a topological representation of the environment, employing a hierarchical representation to capture complex relationships and dependencies. The core mechanism involves a dynamically adjusted policy based on this topological structure, allowing the agent to efficiently explore and react to changes in the environment. We demonstrate the effectiveness of this algorithm through several illustrative scenarios, showcasing its ability to surpass traditional reinforcement learning methods in terms of sample efficiency and robustness. The proposed algorithm provides a promising avenue for developing more sophisticated and adaptable reinforcement learning agents capable of navigating complex and evolving environments.