Publications (11)
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
Chenguang Wang, Yaodong Yang, Oliver Slumbers +4
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-bas…
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Yaodong Yang, Jun Luo, Ying Wen +5
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum f…
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Oliver Slumbers, David Henry Mguni, Stephen Marcus McAleer +3
In order for agents in multi-agent systems (MAS) to be safe, they need to take into account the risks posed by the actions of other agents. However, the dominant paradigm in game t…
Online Double Oracle
Le Cong Dinh, Yaodong Yang, Stephen McAleer +6
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learnin…
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Lukas Schäfer, Oliver Slumbers, Stephen McAleer +3
Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL alg…
Downside Risk-Aware Equilibria for Strategic Decision-Making
Oliver Slumbers, Benjamin Patrick Evans, Sumitra Ganesh +1
Game theory has traditionally had a relatively limited view of risk based on how a player's expected reward is impacted by the uncertainty of the actions of other players. Recently…
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
David Henry Mguni, Taher Jafferjee, Jianhong Wang +7
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to joint…
Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
Oliver Slumbers, Joel Z. Leibo, Marco A. Janssen
Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as…
Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints
David Mguni, Aivar Sootla, Juliusz Ziomek +4
Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at…
Modelling Behavioural Diversity for Learning in Open-Ended Games
Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers +3
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissor…
Neural Auto-Curricula
Xidong Feng, Oliver Slumbers, Ziyu Wan +5
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered…