Collaborative Cost Multi-Agent Decision-Making Algorithm with Factored-Value Monte Carlo Tree Search and Max-Plus

Nii-Emil Alexander-Reindorf and Paul Cotae ()
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Nii-Emil Alexander-Reindorf: Department of Computer Science, School of Engineering and Applied Sciences, The University of the District of Columbia, Washington, DC 20008, USA
Paul Cotae: Department of Electrical and Computer Engineering, School of Engineering and Applied Sciences, The University of the District of Columbia, Washington, DC 20008, USA

Games, 2023, vol. 14, issue 6, 1-20

Abstract: In this paper, we describe the Factored Value MCTS Hybrid Cost-Max-Plus algorithm, a collection of decision-making algorithms (centralized, decentralized, and hybrid) for a multi-agent system in a collaborative setting that considers action costs. Our proposed algorithm is made up of two steps. In the first step, each agent searches for the best individual actions with the lowest cost using the Monte Carlo Tree Search (MCTS) algorithm. Each agent’s most promising activities are chosen and presented to the team. The Hybrid Cost Max-Plus method is utilized for joint action selection in the second step. The Hybrid Cost Max-Plus algorithm improves the well-known centralized and distributed Max-Plus algorithm by incorporating the cost of actions in agent interactions. The Max-Plus algorithm employed the Coordination Graph framework, which exploits agent dependencies to decompose the global payoff function as the sum of local terms. In terms of the number of agents and their interactions, the suggested Factored Value MCTS-Hybrid Cost-Max-Plus method is online, anytime, distributed, and scalable. Our contribution competes with state-of-the-art methodologies and algorithms by leveraging the locality of agent interactions for planning and acting utilizing MCTS and Max-Plus algorithms.

Keywords: Monte Carlo Tree Search; MCTS; Multi-Agent System; real-time decision making; distributed coordination; planning algorithm (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
Date: 2023
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