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Joseph Suarez

4 accepted papers

2023

Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning

Joseph Suarez, David Bloomin, Kyoung Whan Choe, Hao Xiang Li, Ryan Sullivan, Nishaanth Kanna Ravichandran +10
NeurIPS 2023poster

Neural MMO 2.0 is a massively multi-agent and multi-task environment for reinforcement learning research. This version features a novel task-system that broadens the range of training settings and poses a new challenge in generalization: evaluation on and against tasks, maps, and opponents never see…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2023

Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks

Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John P Dickerson, Joseph Suarez
NeurIPS 2023poster

Most reinforcement learning methods rely heavily on dense, well-normalized environment rewards. DreamerV3 recently introduced a model-based method with a number of tricks that mitigate these limitations, achieving state-of-the-art on a wide range of benchmarks with a single set of hyperparameters. T…

Cited by 5Source ↗PDF ↗Scholar ↗
2021

The Neural MMO Platform for Massively Multiagent Research

Joseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola
NeurIPS 2021poster

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environments feature subsets of these properties, but Neural MMO is the first to combine them all. We present Neural MMO as fre…

Cited by 31Source ↗Scholar ↗