# Pok'eChamp: an Expert-level Minimax Language Agent

arXiv: https://arxiv.org/abs/2503.04094 · HTML: https://mlsanity.com/paper/2503.04094/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Placed in

- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Training Language Models to Self-Correct via Reinforcement Learning](https://mlsanity.com/paper/2409.12917/) (2024)
- [Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training](https://mlsanity.com/paper/2309.17179/) (2023)
- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [The NetHack Learning Environment](https://mlsanity.com/paper/2006.13760/) (2020)
- [A Survey on Large Language Model-Based Game Agents](https://mlsanity.com/paper/2404.02039/) (2024)
- [Motif: Intrinsic Motivation from Artificial Intelligence Feedback](https://mlsanity.com/paper/2310.00166/) (2023)
- [Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach](https://mlsanity.com/paper/2312.11865/) (2023)
- [Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Game](https://mlsanity.com/paper/2312.17515/) (2023)
- [Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents](https://mlsanity.com/paper/2403.00690/) (2024)
- [Reflect-RL: Two-Player Online RL Fine-Tuning for LMs](https://mlsanity.com/paper/2402.12621/) (2024)
- [FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.02081/) (2024)
- [Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models](https://mlsanity.com/paper/2311.05720/) (2023)

## Built on by (most cited first, 16 of 16)

- [Continual Harness: Online Adaptation for Self-Improving Foundation Agents](https://mlsanity.com/paper/2605.09998/) (2026)
- [VideoGameBench: Can Vision-Language Models complete popular video games?](https://mlsanity.com/paper/2505.18134/) (2025)
- [GameDevBench: Evaluating Agentic Capabilities Through Game Development](https://mlsanity.com/paper/2602.11103/) (2026)
- [LLM Economist: Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra](https://mlsanity.com/paper/2507.15815/) (2025)
- [OpenGame: Open Agentic Coding for Games](https://mlsanity.com/paper/2604.18394/) (2026)
- [Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications](https://mlsanity.com/paper/2505.00049/) (2025)
- [Learning Game-Playing Agents with Generative Code Optimization](https://mlsanity.com/paper/2508.19506/) (2025)
- [TextAtari: 100K Frames Game Playing with Language Agents](https://mlsanity.com/paper/2506.04098/) (2025)
- [Human-Level Competitive Pok'emon via Scalable Offline Reinforcement Learning with Transformers](https://mlsanity.com/paper/2504.04395/) (2025)
- [Evaluating Language Models' Evaluations of Games](https://mlsanity.com/paper/2510.10930/) (2025)
- [Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning](https://mlsanity.com/paper/2605.00347/) (2026)
- [Implicit Strategic Optimization: Rethinking Long-Horizon Decision-Making in Adversarial Poker Environments](https://mlsanity.com/paper/2602.08041/) (2026)
- [Abstraction Agent](https://mlsanity.com/paper/2609.04303/) (2026)
- [Automatic Generation of High-Performance RL Environments](https://mlsanity.com/paper/2603.12145/) (2026)
- [AgentSociety: Incentivizing Agentic Social Intelligence](https://mlsanity.com/paper/2605.26203/) (2026)
- [PokaiTrainer: Scaling Equilibrium Search to Competitive Pok'emon VGC](https://mlsanity.com/paper/2608.29197/) (2026)

## Abstract (arXiv)

We introduce Pok'eChamp, a minimax agent powered by Large Language Models (LLMs) for Pok'emon battles. Built on a general framework for two-player competitive games, Pok'eChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: (1) player action sampling, (2) opponent modeling, and (3) value function estimation, enabling the agent to effectively utilize gameplay history and human knowledge to reduce the search space and address partial observability. Notably, our framework requires no additional LLM training. We evaluate Pok'eChamp in the popular Gen 9 OU format. When powered by GPT-4o, it achieves a win rate of 76% against the best existing LLM-based bot and 84% against the strongest rule-based bot, demonstrating its superior performance. Even with an open-source 8-billion-parameter Llama 3.1 model, Pok'eChamp consistently outperforms the previous best LLM-based bot, Pok'ellmon powered by GPT-4o, with a 64% win rate. Pok'eChamp attains a projected Elo of 1300-1500 on the Pok'emon Showdown online ladder, placing it among the top 30%-10% of human players. In addition, this work compiles the largest real-player Pok'emon battle dataset, featuring over 3 million games, including more than 500k high-Elo matches. Based on this dataset, we establish a series of battle benchmarks and puzzles to evaluate specific battling skills. We further provide key updates to the local game engine. We hope this work fosters further research that leverage Pok'emon battle as benchmark to integrate LLM technologies with game-theoretic algorithms addressing general multiagent problems. Videos, code, and dataset available at https://sites.google.com/view/pokechamp-llm.