HOAD: The Hanabi Open Agent Dataset
Aron Sarmasi, Timothy Zhang, Chu-Hung Cheng, Huyen Pham, Xuanchen Zhou, Duong Nguyen, Soumil Shekdar, Joshua McCoy · 2021
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.