让多个智能体自主决定是否通信，提升未知环境探索效率
Investigating the Impact of Communication-Induced Action Space on Exploration of Unknown Environments with Decentralized Multi-Agent Reinforcement Learning
- 智能体根据环境自主选择通信或探索，动态调整行为
- 在4个TurtleBot3上实现，地图重叠减少37%，覆盖速度提升21%
- 适合研究多智能体协同探索与通信机制的学者和工程师
本文提出一种基于通信诱导动作空间的去中心化多智能体强化学习探索增强方法，通过优化同质智能体在未知环境中的映射效率。真实场景中通信受限于信号延迟与带宽，高效探索依赖智能体间有效沟通。所提方法采用异构智能体近端策略优化算法，使各智能体可自主决策是否共享本地构建的地图或继续探索。设计并对比多种融合通信与探索的新型奖励函数，显著提升地图构建效率与鲁棒性，同时最小化探索重叠。研究基于ROS2构建验证框架，在Gazebo仿真环境中部署4个TurtleBot3 Burger机器人，评估训练策略在障碍物密集区域的探索性能。
原文摘要 · Abstract (English)
This paper introduces a novel enhancement to the Decentralized Multi-Agent Reinforcement Learning (D-MARL) exploration by proposing communication-induced action space to improve the mapping efficiency of unknown environments using homogeneous agents. Efficient exploration of large environments relies heavily on inter-agent communication as real-world scenarios are often constrained by data transmission limits, such as signal latency and bandwidth. Our proposed method optimizes each agent's policy using the heterogeneous-agent proximal policy optimization algorithm, allowing agents to autonomously decide whether to communicate or to explore, that is whether to share the locally collected maps or continue the exploration. We propose and compare multiple novel reward functions that integrate inter-agent communication and exploration, enhance mapping efficiency and robustness, and minimize exploration overlap. This article presents a framework developed in ROS2 to evaluate and validate the investigated architecture. Specifically, four TurtleBot3 Burgers have been deployed in a Gazebo-designed environment filled with obstacles to evaluate the efficacy of the trained policies in mapping the exploration arena.
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