activity
20192026
most citedDealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

125 citations · 125 across the 2 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

JaxAHT: A JAX-Based Library for Ad Hoc Teamwork

Caroline Wang, Rolando Fernandez, Zelal Su Mustafaoglu +9

Ad Hoc Teamwork (AHT) addresses the challenge of designing agents capable of coordinating with novel partners without prior coordination. However, progress in the field is hindered…

cs.AI2025

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…

cs.AI2025

VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pokémon

Cameron Angliss, Jiaxun Cui, Jiaheng Hu +2

Developing AI agents that can robustly adapt to varying strategic landscapes without retraining is a central challenge in multi-agent learning. Pokémon Video Game Championships (VG…

cs.AI2025

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…

cs.AI2024

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…

cs.AI2023

Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents

Arrasy Rahman, Jiaxun Cui, Peter Stone

Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Team…