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arXiv · 2603.02154

Boltzmann-based Exploration for Robust Decentralized Multi-Agent Planning (Extended Version)

Nhat D. A. Nguyen·Duong D. Nguyen·Gianluca Rizzo·Hung X. Nguyen
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Abstract

Decentralized Monte Carlo Tree Search (Dec-MCTS) is widely used for cooperative multi-agent planning but struggles in sparse or skewed reward environments. We introduce Coordinated Boltzmann MCTS (CB-MCTS), which replaces deterministic UCT with a stochastic Boltzmann policy and a decaying entropy bonus for sustained yet focused exploration. While Boltzmann exploration has been studied in single-agent MCTS, applying it in multi-agent systems poses unique challenges. CB-MCTS is the first to address this. We analyze CB-MCTS in the simple-regret setting and show in simulations that it outperforms Dec-MCTS in deceptive scenarios and remains competitive on standard benchmarks, providing a robust solution for multi-agent planning.

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Nhat D. A. Nguyen, Duong D. Nguyen, Gianluca Rizzo, Hung X. Nguyen. 2026-03-02. Boltzmann-based Exploration for Robust Decentralized Multi-Agent Planning (Extended Version). https://arxiv.org/abs/2603.02154

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