3 citations · 4 across the 10 of their papers we have counts for
3 papers · 1 filter
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers
Chace Ashcraft, Ted Staley, Josh Carney +4
Backdoor attacks, or trojans, pose a security risk by concealing undesirable behavior in deep neural network models. Open-source neural networks are downloaded from the internet da…
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Vindula Jayawardana, Baptiste Freydt, Ao Qu +3
Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited. A key cha…
Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning
Brett Daley, Cameron Hickert, Christopher Amato
Deep Reinforcement Learning (RL) methods rely on experience replay to approximate the minibatched supervised learning setting; however, unlike supervised learning where access to l…