4 papers
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +39
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
Semantic World Models
Jacob Berg, Chuning Zhu, Yanda Bao +2
Planning with world models offers a powerful paradigm for robotic control. Conventional approaches train a model to predict future frames conditioned on current frames and actions,…
Sequence Modeling for N-Agent Ad Hoc Teamwork
Caroline Wang, Di Yang Shi, Elad Liebman +3
N-agent ad hoc teamwork (NAHT) is a newly introduced challenge in multi-agent reinforcement learning, where controlled subteams of varying sizes must dynamically collaborate with v…
N-Agent Ad Hoc Teamwork
Caroline Wang, Arrasy Rahman, Ishan Durugkar +2
Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learn…