15 citations · 16 across the 9 of their papers we have counts for
10 papers
VAM: Verbalized Action Masking for Controllable Exploration in RL Post-Training -- A Chess Case Study
Zhicheng Zhang, Ziyan Wang, Yali Du +1
Exploration remains a key bottleneck for reinforcement learning (RL) post-training of large language models (LLMs), where sparse feedback and large action spaces can lead to premat…
Evaluating Collective Behaviour of Hundreds of LLM Agents
Richard Willis, Jianing Zhao, Yali Du +1
LLM-powered AI assistants acting on behalf of users can produce poor collective outcomes at scale. We introduce a framework for evaluating their emergent behaviour in social dilemm…
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…
SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Zihao Guo, Shuqing Shi, Richard Willis +3
Sequential social dilemmas pose a significant challenge in the field of multi-agent reinforcement learning (MARL), requiring environments that accurately reflect the tension betwee…
Will Systems of LLM Agents Cooperate: An Investigation into a Social Dilemma
Richard Willis, Yali Du, Joel Z Leibo +1
As autonomous agents become more prevalent, understanding their collective behaviour in strategic interactions is crucial. This study investigates the emergent cooperative tendenci…
Quantifying the Self-Interest Level of Markov Social Dilemmas
Richard Willis, Yali Du, Joel Z Leibo +1
This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas. We extend the concept of self-interest level from normal-form games to Markov…
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
Yang Li, Wenhao Zhang, Jianhong Wang +4
Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective…
Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
Nam Phuong Tran, The Anh Ta, Shuqing Shi +3
Reward allocation, also known as the credit assignment problem, has been an important topic in economics, engineering, and machine learning. An important concept in reward allocati…
A Review of Cooperation in Multi-agent Learning
Yali Du, Joel Z. Leibo, Usman Islam +2
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines, including game theory, economics, social sciences, and evolutionary biology. Resea…
Resolving social dilemmas with minimal reward transfer
Richard Willis, Yali Du, Joel Z Leibo +1
Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Conseque…