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Constrained Stochastic Optimal Control with a Baseline Performance Guarantee
Yinlam Chow, Mohammad Ghavamzadeh
In this paper, we show how a simulated Markov decision process (MDP) built by the so-called \emph{baseline} policies, can be used to compute a different policy, namely the \emph{si…
Algorithms for CVaR Optimization in MDPs
Yinlam Chow, Mohammad Ghavamzadeh
In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion. Conditiona…
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions
Amir-massoud Farahmand, Doina Precup, André M. S. Barreto +1
Tackling large approximate dynamic programming or reinforcement learning problems requires methods that can exploit regularities, or intrinsic structure, of the problem in hand. Mo…
Variance-Constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs
Prashanth L. A., Mohammad Ghavamzadeh
In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in rewards in addition to maximizing a standard criterion. Variance…