34 citations · 52 across the 3 of their papers we have counts for
3 papers
cs.LG2017★ 7 cited
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality
Woon Sang Cho, Mengdi Wang
We develop a parameterized Primal-Dual Learning method based on deep neural networks for Markov decision process with large state space and off-policy reinforcement learning. I…
cs.LG2017★ 34 cited
Primal-Dual Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problems
Mengdi Wang
Consider the problem of approximating the optimal policy of a Markov decision process (MDP) by sampling state transitions. In contrast to existing reinforcement learning methods th…
cs.CC2017★ 11 cited
Lower Bound On the Computational Complexity of Discounted Markov Decision Problems
Yichen Chen, Mengdi Wang
We study the computational complexity of the infinite-horizon discounted-reward Markov Decision Problem (MDP) with a finite state space and a finite action space $|…