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Found 5 papers, 3 papers with code
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games
Reinforcement Learning- (RL-)based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation.
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent.
Learning Models of Adversarial Agent Behavior under Partial Observability
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction.
MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving
We find that our approach generates driving styles consistent with end-user styles (p<. 001) and participants rate our approach as more similar to their level of aggressiveness (p=. 002).
Meta-active Learning in Probabilistically-Safe Optimization
Learning to control a safety-critical system with latent dynamics (e. g. for deep brain stimulation) requires taking calculated risks to gain information as efficiently as possible.
