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

arXiv: https://arxiv.org/abs/2603.02154 · HTML: https://mlsanity.com/paper/2603.02154/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [Boltzmann Exploration Done Right](https://mlsanity.com/paper/1705.10257/) (2017)
- [Monte Carlo Tree Search with Boltzmann Exploration](https://mlsanity.com/paper/2404.07732/) (2024)
- [United We Stand: Decentralized Multi-Agent Planning With Attrition](https://mlsanity.com/paper/2407.08254/) (2024)

## Abstract (arXiv)

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.