HOAD: The Hanabi Open Agent Dataset

Aron Sarmasi (University of California, Davis), Timothy Zhang (University of California, Davis), Chu-Hung Cheng (University of California, Davis), Huyen Pham (University of California, Davis), Xuanchen Zhou (University of California, Davis), Duong Nguyen (University of California, Davis), Soumil Shekdar (University of California, Davis), Joshua McCoy (University of California, Davis)

Abstract

In this work we present the Hanabi Open Agent Dataset (HOAD)meant to address the current lack of Hanabi datasets, HOAD is an easily extensible, open-sourced, and comprehensive collection of existing Hanabi playing agents, all ported to the Hanabi Learning Environment (HLE). We give a description and analysis of each agent's strategy, and we also show cross-play performance between all the agents, demonstrating both their high quality and diversity of strategy. These properties make HOAD especially well suited to studies involving meta-learning and transfer learning. Finally, we describe in detail an easy way to add new agents to HOAD regardless of the origin codebase of the agent and make our code and dataset publicly available at https://github.com/aronsar/hoad.