20 citations · 43 across the 19 of their papers we have counts for
4 papers · 1 filter
Meta Arcade: A Configurable Environment Suite for Meta-Learning
Edward W. Staley, Chace Ashcraft, Benjamin Stoler +4
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time. As a result, most existing research benchmarks consist of individual games or suites…
Machine Learning aided Crop Yield Optimization
Chace Ashcraft, Kiran Karra
We present a crop simulation environment with an OpenAI Gym interface, and apply modern deep reinforcement learning (DRL) algorithms to optimize yield. We empirically show that DRL…
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks
Kiran Karra, Chace Ashcraft, Cash Costello
Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn genera…
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
Chace Ashcraft, Kiran Karra
In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are tri…