Publications (7)
Exploring Consequences of Privacy Policies with Narrative Generation via Answer Set Programming
Chinmaya Dabral, Emma Tosch, Chris Martens
Informed consent has become increasingly salient for data privacy and its regulation. Entities from governments to for-profit companies have addressed concerns about data privacy w…
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…
Measuring and Characterizing Generalization in Deep Reinforcement Learning
Sam Witty, Jun Ki Lee, Emma Tosch +3
Deep reinforcement-learning methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has…
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: 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…
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…