2 citations · 3 across the 5 of their papers we have counts for
5 papers
Ad-Hoc Human-AI Coordination Challenge
Tin Dizdarević, Ravi Hammond, Tobias Gessler +7
Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge. Hanabi is a cooperative card game…
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
Matteo Gallici, Ivan Masmitja, Mario Martín
Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking. Reinforcement learning (RL) has emerged as a powerful method for cont…
Simplifying Deep Temporal Difference Learning
Matteo Gallici, Mattie Fellows, Benjamin Ellis +4
Q-learning played a foundational role in the field reinforcement learning (RL). However, TD algorithms with off-policy data, such as Q-learning, or nonlinear function approximation…
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally,…
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems
Matteo Gallici, Mario Martin, Ivan Masmitja
Coordination is one of the most difficult aspects of multi-agent reinforcement learning (MARL). One reason is that agents normally choose their actions independently of one another…