31 citations · 58 across the 4 of their papers we have counts for
3 papers · 1 filter
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
Akanksha Atrey, Kaleigh Clary, David Jensen
Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents. However, a review of how saliency maps are used in practice in…
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…