Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from $\text{A}^*$ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems.

Investigating the Impact of Communication-Induced Action Space on Exploration of Unknown Environments with Decentralized Multi-Agent Reinforcement Learning

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.