Multi-Agent Reinforcement Learning: Methods, Applications, Prospects, and Challenges

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Introduction ‌

Numerous applications of Reinforcement Learning have displayed notable potential ⁠ in tackling sequence decision tasks in different fields. Various fields like game playing and robotics ⁠ have thoroughly examined this area. Many agents are used in MARL ⁠ to optimize their potential rewards. Though significant success has been attained, scaling, ⁠ non-stationarity, and credibility remain significant issues. We offer a comprehensive review of MARL methods, ⁠ application scenarios, and prospects in this paper. Emphasizes the necessity of reliable MARL and examines its utilization ⁠ in human-machine interaction, discussing the difficulties related to it.

Methods

The article introduces SARL and MARL, which are ⁠ both single and multi-agent reinforcement learning approaches. To maximize the potential total discounted reward, SARL ⁠ focuses on learning an optimal policy. Describes the yearly rainfall levels of various cities and introduces Deep ⁠ Q-Network and Policy Gradient as prominent methods in this work. ‌

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MARL Applications across multiple agents are ⁠ presented in this table. ​

The publication showcases diverse scenarios where ⁠ MARL has been effectively implemented. The applications across multiple domains including smart transportation, ⁠ education, manufacturing, and security are showcased here. ‌

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Prognostic prospects for multi-agent ⁠ reinforcement learning. ‍

The paper examines visionary prospects for MARL and emphasizes ⁠ the importance of reliable MARL in practical applications. It explores safety, robustness, generalization, and learning with ethical ⁠ constraints as core concepts of trustworthy MARL. ‍

Examining the obstacles surrounding the enhancement of ⁠ multi-agent reinforcement learning from human viewpoint. ‌

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To understand human-MARL challenges in various interaction ⁠ scenarios, the paper delves in. Analyzes non-Markovian human behavior due to external factors, diversity of human behavior, ⁠ complexity of interactions, and scalability among multiple humans and machines. ‍

Conclusion ⁠

The article summarizes the reviewed techniques, applications, ⁠ and challenges in Multi-Agent Reinforcement Learning. Building trustable MARL frameworks is emphasized to ⁠ ensure success in diverse applications. The significance of taking human interaction into account when ⁠ developing MARL to enhance future interactions is emphasized. ⁠

Reference

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