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Found 6 papers, 0 papers with code
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance
We then apply these tools to single-agent and multi-agent environments to show the impact of introducing distribution shifts during test time.
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning
Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration.
Ad Hoc Teamwork in the Presence of Adversaries
Advances in ad hoc teamwork have the potential to create agents that collaborate robustly in real-world applications.
Reward-Free Attacks in Multi-Agent Reinforcement Learning
We investigate how effective an attacker can be when it only learns from its victim's actions, without access to the victim's reward.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Adversarial Attacks in Cooperative AI
Single-agent reinforcement learning algorithms in a multi-agent environment are inadequate for fostering cooperation.
How Can Creativity Occur in Multi-Agent Systems?
Complex systems show how surprising and beautiful phenomena can emerge from structures or agents following simple rules.
