48 citations · 69 across the 6 of their papers we have counts for
5 papers · 1 filter
The Neural MMO Platform for Massively Multiagent Research
Joseph Suarez, Yilun Du, Clare Zhu +2
Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular game systems. Existing environ…
Improved Contrastive Divergence Training of Energy Based Models
Yilun Du, Shuang Li, Joshua Tenenbaum +1
Contrastive divergence is a popular method of training energy-based models, but is known to have difficulties with training stability. We propose an adaptation to improve contrasti…
Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
Joseph Suarez, Yilun Du, Igor Mordatch +1
Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a…
Model Based Planning with Energy Based Models
Yilun Du, Toru Lin, Igor Mordatch
Model-based planning holds great promise for improving both sample efficiency and generalization in reinforcement learning (RL). We show that energy-based models (EBMs) are a promi…
Implicit Generation and Generalization in Energy-Based Models
Yilun Du, Igor Mordatch
Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to sca…