1 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense
Sayak Mukherjee, Samrat Chatterjee, Emilie Purvine +2
Designing rewards for autonomous cyber attack and defense learning agents in a complex, dynamic environment is a challenging task for subject matter experts. We propose a large lan…
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance
Ted Fujimoto, Joshua Suetterlein, Samrat Chatterjee +1
Research in machine learning is making progress in fixing its own reproducibility crisis. Reinforcement learning (RL), in particular, faces its own set of unique challenges. Compar…
Reward-Free Attacks in Multi-Agent Reinforcement Learning
Ted Fujimoto, Timothy Doster, Adam Attarian +2
We investigate how effective an attacker can be when it only learns from its victim's actions, without access to the victim's reward. In this work, we are motivated by the scenario…
Adversarial Attacks in Cooperative AI
Ted Fujimoto, Arthur Paul Pedersen
Single-agent reinforcement learning algorithms in a multi-agent environment are inadequate for fostering cooperation. If intelligent agents are to interact and work together to sol…