Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games

1 code implementation • 16 Mar 2024 • Zixuan Wu, Sean Ye, Manisha Natarajan, Matthew C. Gombolay

Reinforcement Learning- (RL-)based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation.

Autonomous Navigation Efficient Exploration +4

Diffusion Models for Multi-target Adversarial Tracking

1 code implementation • 12 Jul 2023 • Sean Ye, Manisha Natarajan, Zixuan Wu, Matthew Gombolay

Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited.

Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment

1 code implementation • 20 Jun 2023 • Zixuan Wu, Sean Ye, Manisha Natarajan, Letian Chen, Rohan Paleja, Matthew C. Gombolay

We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent.

reinforcement-learning Reinforcement Learning (RL)

Learning Models of Adversarial Agent Behavior under Partial Observability

1 code implementation • 19 Jun 2023 • Sean Ye, Manisha Natarajan, Zixuan Wu, Rohan Paleja, Letian Chen, Matthew C. Gombolay

The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction.