5 citations · 6 across the 4 of their papers we have counts for
4 papers
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
Lorenzo Magnino, Kai Shao, Zida Wu +2
Mean field games (MFGs) have emerged as a powerful framework for modeling interactions in large-scale multi-agent systems. Despite recent advancements in reinforcement learning (RL…
Reinforcement Learning for Finite Space Mean-Field Type Games
Kai Shao, Jiacheng Shen, Mathieu Laurière
Mean field type games (MFTGs) describe Nash equilibria between large coalitions: each coalition consists of a continuum of cooperative agents who maximize the average reward of the…
Vis2Mus: Exploring Multimodal Representation Mapping for Controllable Music Generation
Runbang Zhang, Yixiao Zhang, Kai Shao +2
In this study, we explore the representation mapping from the domain of visual arts to the domain of music, with which we can use visual arts as an effective handle to control musi…
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning
Che Wang, Shuhan Yuan, Kai Shao +1
A simple and natural algorithm for reinforcement learning (RL) is Monte Carlo Exploring Starts (MCES), where the Q-function is estimated by averaging the Monte Carlo returns, and t…