Aug 1, 2016 · Convex synthesis of optimal policies for Markov Decision Processes with sequentially-observed transitions. Abstract: This paper extends ...
This paper extends finite state and action space Markov Decision Process (MDP) models by introducing a new type of measurement for the outcomes of actions.
The new measurement allows to sequentially observe the next-state transition for taking an action, i.e., the actions are ordered and the next action outcome in ...
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This new MDP model with sequential measurements is referred to as sequentially-observed MDP (SO-MDP). We show that the SO-MDP shares some similar properties ...
The proposed Markov model is applicable to both decisionmaking for single and multi-agent systems in stochastic environments. Our particular interest is ...
Dec 13, 2023 · First, as in MDPs, the convex formulation can provide crucial insights into the structure of optimal policies and value functions.
“Convex synthesis of optimal policies for Markov decision processes with sequentially-observed transitions,” in American Control Conference (ACC), pp. 3862 ...
Oct 19, 2015 · An efficient algorithm based on Linear Programming (LP) and duality theory is proposed, which gives the convex set of feasible policies and ...
We study the problem of policy synthesis for uncer- tain partially observable Markov decision processes. (uPOMDPs). The transition probability function of.
Convex synthesis of optimal policies for Markov Decision Processes with sequentially-observed transitions · Robust Action Selection in Partially Observable ...