80 citations · 155 across the 7 of their papers we have counts for
7 papers
Processing Network Controls via Deep Reinforcement Learning
Mark Gluzman
Novel advanced policy gradient (APG) algorithms, such as proximal policy optimization (PPO), trust region policy optimization, and their variations, have become the dominant reinfo…
Refined Policy Improvement Bounds for MDPs
J. G. Dai, Mark Gluzman
The policy improvement bound on the difference of the discounted returns plays a crucial role in the theoretical justification of the trust-region policy optimization (TRPO) algori…
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…
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…
Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory
Mark Gluzman, Jacob G. Scott, Alexander Vladimirsky
Recent clinical trials have shown that the adaptive drug therapy can be more efficient than a standard MTD-based policy in treatment of cancer patients. The adaptive therapy paradi…
Dynamics of Two Coupled van der Pol Oscillators with Delay Coupling Revisited
Mark Gluzman, Richard Rand
The problem of two van der Pol oscillators coupled by velocity delay terms was studied by Wirkus and Rand in 2002. The small-epsilon analysis resulted in a slow flow which containe…
Uniform Global Attractors for Non-Autonomous Dissipative Dynamical Systems
Michael Zgurovsky, Mark Gluzman, Nataliia Gorban +3
In this paper we consider sufficient conditions for the existence of uniform compact global attractor for non-autonomous dynamical systems in special classes of infinite-dimensiona…