# A Fast Evolutionary adaptation for MCTS in Pommerman

arXiv: https://arxiv.org/abs/2111.13770 · HTML: https://mlsanity.com/paper/2111.13770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Pommerman: A Multi-Agent Playground](https://mlsanity.com/paper/1809.07124/) (2018)
- [Rolling Horizon Evolutionary Algorithms for General Video Game Playing](https://mlsanity.com/paper/2003.12331/) (2020)
- [Real-time tree search with pessimistic scenarios](https://mlsanity.com/paper/1902.10870/) (2019)

## Abstract (arXiv)

Artificial Intelligence, when amalgamated with games makes the ideal structure for research and advancing the field. Multi-agent games have multiple controls for each agent which generates huge amounts of data while increasing search complexity. Thus, we need advanced search methods to find a solution and create an artificially intelligent agent. In this paper, we propose our novel Evolutionary Monte Carlo Tree Search (FEMCTS) agent which borrows ideas from Evolutionary Algorthims (EA) and Monte Carlo Tree Search (MCTS) to play the game of Pommerman. It outperforms Rolling Horizon Evolutionary Algorithm (RHEA) significantly in high observability settings and performs almost as well as MCTS for most game seeds, outperforming it in some cases.