60 citations · 94 across the 5 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
math.PR2020★ 5 cited
High order steady-state diffusion approximations
Anton Braverman, J. G. Dai, Xiao Fang
We derive and analyze new diffusion approximations of stationary distributions of Markov chains that are based on second- and higher-order terms in the expansion of the Markov chai…
math.OC2020★ 12 cited
Scalable Deep Reinforcement Learning for Ride-Hailing
Jiekun Feng, Mark Gluzman, J. G. Dai
Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to meet ride requests throughout the day. We consider a Markov decision process (MDP) model o…
math.OC2020★ 60 cited
Queueing Network Controls via Deep Reinforcement Learning
J. G. Dai, Mark Gluzman
Novel advanced policy gradient (APG) methods, such as Trust Region policy optimization and Proximal policy optimization (PPO), have become the dominant reinforcement learning algor…