14 citations · 38 across the 12 of their papers we have counts for
6 papers · 1 filter
Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Joseph Suárez, Phillip Isola, Kyoung Whan Choe +15
Neural MMO 2.0 is a massively multi-agent environment for reinforcement learning research. The key feature of this new version is a flexible task system that allows users to define…
The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade
Enhong Liu, Joseph Suarez, Chenhui You +20
In this paper, we present the results of the NeurIPS-2022 Neural MMO Challenge, which attracted 500 participants and received over 1,600 submissions. Like the previous IJCAI-2022 N…
Using Human Feedback to Fine-tune Diffusion Models without Any Reward Model
Kai Yang, Jian Tao, Jiafei Lyu +6
Using reinforcement learning with human feedback (RLHF) has shown significant promise in fine-tuning diffusion models. Previous methods start by training a reward model that aligns…
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
Yangkun Chen, Joseph Suarez, Junjie Zhang +18
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-a…
Recon: Reducing Conflicting Gradients from the Root for Multi-Task Learning
Guangyuan Shi, Qimai Li, Wenlong Zhang +2
A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gra…
Emergent collective intelligence from massive-agent cooperation and competition
Hanmo Chen, Stone Tao, Jiaxin Chen +6
Inspired by organisms evolving through cooperation and competition between different populations on Earth, we study the emergence of artificial collective intelligence through mass…