98 citations · 851 across the 68 of their papers we have counts for
6 papers · 1 filter
Observational Overfitting in Reinforcement Learning
Xingyou Song, Yiding Jiang, Stephen Tu +2
A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the…
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…
An Empirical Study on Hyperparameters and their Interdependence for RL Generalization
Xingyou Song, Yilun Du, Jacob Jackson
Recent results in Reinforcement Learning (RL) have shown that agents with limited training environments are susceptible to a large amount of overfitting across many domains. A key…
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
Yilun Du, Karthik Narasimhan
While model-based deep reinforcement learning (RL) holds great promise for sample efficiency and generalization, learning an accurate dynamics model is often challenging and requir…
Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
Joseph Suarez, Yilun Du, Phillip Isola +1
The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial…
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…