2 citations · 3 across the 4 of their papers we have counts for
6 papers
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
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…
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork
Caroline Wang, Arrasy Rahman, Benjamin Nativi +4
Learning to collaborate with previously unseen partners is a fundamental generalization challenge, known as Ad Hoc Teamwork (AHT). Existing methods often adopt a two-stage pipeline…
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
Zizhao Wang, Caroline Wang, Xuesu Xiao +2
Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of…
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning
Caroline Wang, Garrett Warnell, Peter Stone
While combining imitation learning (IL) and reinforcement learning (RL) is a promising way to address poor sample efficiency in autonomous behavior acquisition, methods that do so…
DM: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching
Caroline Wang, Ishan Durugkar, Elad Liebman +1
Current approaches to multi-agent cooperation rely heavily on centralized mechanisms or explicit communication protocols to ensure convergence. This paper studies the problem of di…