16 citations · 30 across the 7 of their papers we have counts for
4 papers · 1 filter
PlanAlyzer: Assessing Threats to the Validity of Online Experiments
Emma Tosch, Eytan Bakshy, Emery D. Berger +2
Online experiments are ubiquitous. As the scale of experiments has grown, so has the complexity of their design and implementation. In response, firms have developed software frame…
Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning
Emma Tosch, Kaleigh Clary, John Foley +1
Evaluation of deep reinforcement learning (RL) is inherently challenging. In particular, learned policies are largely opaque, and hypotheses about the behavior of deep RL agents ar…
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
Kaleigh Clary, Emma Tosch, John Foley +1
Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itse…
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
John Foley, Emma Tosch, Kaleigh Clary +1
It is a widely accepted principle that software without tests has bugs. Testing reinforcement learning agents is especially difficult because of the stochastic nature of both agent…