6 citations · 6 across the 2 of their papers we have counts for
6 papers
On the Convergence of Fictitious Play: A Decomposition Approach
Yurong Chen, Xiaotie Deng, Chenchen Li +4
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in -player games, which builds the foundation for modern…
Learning in Nonzero-Sum Stochastic Games with Potentials
David Mguni, Yutong Wu, Yali Du +6
Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by…
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…
Multi-Agent Determinantal Q-Learning
Yaodong Yang, Ying Wen, Liheng Chen +4
Centralized training with decentralized execution has become an important paradigm in multi-agent learning. Though practical, current methods rely on restrictive assumptions to dec…
Coordinating the Crowd: Inducing Desirable Equilibria in Non-Cooperative Systems
David Mguni, Joel Jennings, Sergio Valcarcel Macua +3
Many real-world systems such as taxi systems, traffic networks and smart grids involve self-interested actors that perform individual tasks in a shared environment. However, in suc…
Decentralised Learning in Systems with Many, Many Strategic Agents
David Mguni, Joel Jennings, Enrique Munoz de Cote
Although multi-agent reinforcement learning can tackle systems of strategically interacting entities, it currently fails in scalability and lacks rigorous convergence guarantees. C…