Exploring Consequences of Privacy Policies with Narrative Generation via Answer Set Programming

no code implementations • 13 Dec 2022 • Chinmaya Dabral, Emma Tosch, Chris Martens

Informed consent has become increasingly salient for data privacy and its regulation.

Toybox: A Suite of Environments for Experimental Evaluation of Deep Reinforcement Learning

2 code implementations • 7 May 2019 • Emma Tosch, Kaleigh Clary, John Foley, David Jensen

We show that TOYBOX enables a wide range of experiments and analyses that are impossible in other environments.

reinforcement-learning Reinforcement Learning (RL)

Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments

1 code implementation • 12 Apr 2019 • Kaleigh Clary, Emma Tosch, John Foley, David Jensen

Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itself have led researchers to report the performance of learned agents using aggregate metrics of performance over multiple random seeds for a single environment.

Atari Games reinforcement-learning +1

Measuring and Characterizing Generalization in Deep Reinforcement Learning

no code implementations • 7 Dec 2018 • Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen

We re-examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on-policy, off-policy, and unreachable states.

reinforcement-learning Reinforcement Learning (RL) +1

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

3 code implementations • 6 Dec 2018 • John Foley, Emma Tosch, Kaleigh Clary, David Jensen

It is a widely accepted principle that software without tests has bugs.

Atari Games reinforcement-learning +1