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cs.LG2021
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…
cs.MA2021
How Can Creativity Occur in Multi-Agent Systems?
Ted Fujimoto
Complex systems show how surprising and beautiful phenomena can emerge from structures or agents following simple rules. With the recent success of deep reinforcement learning (RL)…
cs.LG2021
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…