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A Cooperative Multi-Agent Probabilistic Framework for Search and Track Missions

2023-02-21 · Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou, Marios M. Polycarpou

In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed. Specifically, we study the problem of cooperative searching and tracking of multiple moving targets by a group of autonomous mobile agents with limited sensing capabilities. We assume that the actual number of targets present is not known a priori and that target births/deaths can occur anywhere inside the surveillance region thus efficient search strategies are required to detect and track as many targets as possible. To address the aforementioned challenges we recursively compute and propagate in time the searching-and-tracking (SAT) density. Using the SAT-density, we then develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.

📄 PDF Abstract BibTeX arXiv:2302.10723

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Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning

2023-07-11 · Tomoaki Nakamura, Akira Taniguchi, Tadahiro Taniguchi

This paper proposes a generative probabilistic model integrating emergent communication and multi-agent reinforcement learning. The agents plan their actions by probabilistic inference, called control as inference, and c…

Multi-agent Reinforcement Learning

Maximum Entropy Heterogeneous-Agent Reinforcement Learning

2023-06-19 · Jiarong Liu, Yifan Zhong, Siyi Hu, Haobo Fu 외

Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample complexity, training instability, and…

MuJoCo Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning

2021-04-14 · Yuan Pu, Shaochen Wang, Rui Yang, Xin Yao 외

Deep reinforcement learning methods have shown great performance on many challenging cooperative multi-agent tasks. Two main promising research directions are multi-agent value function decomposition and multi-agent poli…

counterfactual Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning+4

Efficient Querying for Cooperative Probabilistic Commitments

2020-12-14 · Qi Zhang, Edmund H. Durfee, Satinder Singh

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. Agents whose objectives are aligned, and where one agent can help…

Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach

2019-09-25 · Shubham Gupta, Ambedkar Dukkipati

Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train. W…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1