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Average Reward Reinforcement Learning for Semi-Markov Decision Processes
2017In this paper, we study new reinforcement learning (RL) algorithms for Semi-Markov decision processes (SMDPs) with an average reward criterion. Based on the discrete-time type Bellman optimality equation, we use incremental value iteration (IVI), stochastic shortest path (SSP) value iteration and bisection algorithms to derive novel RL algorithms in a ...
Jiayuan Yang +3 more
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On undiscounted semi-Markov decision processes with absorbing states
Mathematical Methods of Operations Research, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Reinforcement learning with options in semi Markov decision processes
2021Treball fi de màster de: Master in Intelligent Interactive ...
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Semi-Markov Decision-Making Processes with Vector Gains
Theory of Probability & Its Applications, 1984Vinogradskaya, T. M. +2 more
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Deterministic policy gradient algorithms for semi‐Markov decision processes
International Journal of Intelligent Systems, 2021Ashkan Haji Hosseinloo +1 more
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On the Optimality Conditions for Semi-Markov Decision Processes
1977The paper presents a recurrence formula for the difference between expected rewards and sojourn times generated by N transitions of a semi-Markov decision process with finite state space. Using the recurrence formula convergence of policy iteration method can be easily verified and also necessary and sufficient optimality conditions for average optimal
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Decision aids for localized prostate cancer treatment choice: Systematic review and meta‐analysis
Ca-A Cancer Journal for Clinicians, 2015Philippe D Violette +2 more
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Implementing and evaluating shared decision making in oncology practice
Ca-A Cancer Journal for Clinicians, 2014Heather Kane, Katherine A Treiman
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What is lacking in current decision aids on cancer screening?
Ca-A Cancer Journal for Clinicians, 2013Masahito Jimbo +2 more
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