Scholarlink

Using autonomous, decentralised control of unmanned aerial vehicles (UAVs) for critical scenarios such as bushfire firefighting provides clear safety and cost benefits. In this paper, we introduce a novel framework for autonomous, decentralised control based on multi-agent reinforcement learning (MARL) and a planning heuristic, designed to support learning a search-and-interact task in a multi-particle environment, which models a range of multi-UAV control applications. Our approach hybridises a planning heuristic with generic MARL methods under a discrete multi-action paradigm. The planning heuristic extracts only focused information from the environment, while also augmenting agent-centric observation data via exploiting observation-action symmetry to improve training. We employ this heuristic within a Centralised and Training Decentralised Execution (CTDE) paradigm to train autonomous control that is agnostic to the complexity of the environment, and effective under a range of action dynamics. The controller is encoded in a lean policy with reduced footprint, and can be deployed within resource-limited devices. Experimental results show that using even simple Q-learners within the framework provides highly effective agents for accurate firefighting in complex scenarios. Our approach is applicable to decision problems with similar search-and-interact characteristics.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Hybrid Multi-Agent Reinforcement Learning Framework for Decentralised Search-And-Interact Tasks Under Partial Observability

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Anh Viet Do,
  • Eranda Galhenage,
  • Aneta Neumann,
  • Frank Neumann,
  • Anton V. Uzunov,
  • Claudia Szabo

摘要

Using autonomous, decentralised control of unmanned aerial vehicles (UAVs) for critical scenarios such as bushfire firefighting provides clear safety and cost benefits. In this paper, we introduce a novel framework for autonomous, decentralised control based on multi-agent reinforcement learning (MARL) and a planning heuristic, designed to support learning a search-and-interact task in a multi-particle environment, which models a range of multi-UAV control applications. Our approach hybridises a planning heuristic with generic MARL methods under a discrete multi-action paradigm. The planning heuristic extracts only focused information from the environment, while also augmenting agent-centric observation data via exploiting observation-action symmetry to improve training. We employ this heuristic within a Centralised and Training Decentralised Execution (CTDE) paradigm to train autonomous control that is agnostic to the complexity of the environment, and effective under a range of action dynamics. The controller is encoded in a lean policy with reduced footprint, and can be deployed within resource-limited devices. Experimental results show that using even simple Q-learners within the framework provides highly effective agents for accurate firefighting in complex scenarios. Our approach is applicable to decision problems with similar search-and-interact characteristics.