398 citations · 488 across the 14 of their papers we have counts for
16 papers
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
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…
-Policy Gradients: A General Framework for Goal Conditioned RL using -Divergences
Siddhant Agarwal, Ishan Durugkar, Peter Stone +1
Goal-Conditioned Reinforcement Learning (RL) problems often have access to sparse rewards where the agent receives a reward signal only when it has achieved the goal, making policy…
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning
Eddy Hudson, Ishan Durugkar, Garrett Warnell +1
Given a dataset of expert agent interactions with an environment of interest, a viable method to extract an effective agent policy is to estimate the maximum likelihood policy indi…