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Kai Shao

2 accepted papers

2025

Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics

NeurIPS 2025poster

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) for MFGs, existing methods are typically limited to finite spaces or stationary models, hindering their applicability…

Cited by 0Source ↗Scholar ↗
2022

On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning

ICLR 2022poster

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 the policy is improved by choosing actions that maximize the current estimate of the Q-function. Exploration is performed…

Cited by 28Source ↗PDF ↗Scholar ↗