10 citations · 33 across the 33 of their papers we have counts for
6 papers · 1 filter
Generalized Intention Modeling in Multi-Agent Reinforcement Learning
Mateusz Odrowaz-Sypniewski, Jasmine Bayrooti, Ajay Shankar +1
Modeling an opponent's intent is critical for effective decision-making in non-cooperative, competitive, and general-sum multi-agent reinforcement learning. Existing opponent model…
Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding
Rishabh Jain, Keisuke Okumura, Michael Amir +2
Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their respective goals without collisions. S…
Scaling Multi-Agent Environment Co-Design with Diffusion Models
Hao Xiang Li, Michael Amir, Amanda Prorok
The agent-environment co-design paradigm jointly optimises agent policies and environment configurations in search of improved system performance. With application domains ranging…
No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
Jasmine Bayrooti, Sattar Vakili, Amanda Prorok +1
Thompson sampling (TS) is a powerful and widely used strategy for sequential decision-making, with applications ranging from Bayesian optimization to reinforcement learning (RL). D…
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok
Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state…
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
Matteo Bettini, Amanda Prorok, Vincent Moens
The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a reproducibility crisis. While solutions for standardized reporting have been proposed to address the is…