4 papers
Targeting World Models to Compromise Robot Learning Pipelines
Ethan Rathbun, Ahmed Agha, Saaduddin Mahmud +3
World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world envi…
Beware Untrusted Simulators -- Reward-Free Backdoor Attacks in Reinforcement Learning
Ethan Rathbun, Wo Wei Lin, Alina Oprea +1
Simulated environments are a key piece in the success of Reinforcement Learning (RL), allowing practitioners and researchers to train decision making agents without running expensi…
Adversarial Inception Backdoor Attacks against Reinforcement Learning
Ethan Rathbun, Alina Oprea, Christopher Amato
Recent works have demonstrated the vulnerability of Deep Reinforcement Learning (DRL) algorithms against training-time, backdoor poisoning attacks. The objectives of these attacks…
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
Ethan Rathbun, Christopher Amato, Alina Oprea
Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications -- making it paramount to ensure the robustness…