Intermittently Observable Markov Decision Processes

• URL: http://arxiv.org/abs/2302.11761v1
• Date: Thu, 23 Feb 2023 03:38:03 GMT
• Title: Intermittently Observable Markov Decision Processes
• Authors: Gongpu Chen and Soung-Chang Liew
• Abstract summary: We consider a scenario where the controller perceives the state information of the process via an unreliable communication channel. The transmissions of state information over the whole time horizon are modeled as a Bernoulli lossy process. We develop two finite-state approximations to the tree MDP to find near-optimal policies efficiently.
• Score: 26.118176084782842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates MDPs with intermittent state information. We consider a scenario where the controller perceives the state information of the process via an unreliable communication channel. The transmissions of state information over the whole time horizon are modeled as a Bernoulli lossy process. Hence, the problem is finding an optimal policy for selecting actions in the presence of state information losses. We first formulate the problem as a belief MDP to establish structural results. The effect of state information losses on the expected total discounted reward is studied systematically. Then, we reformulate the problem as a tree MDP whose state space is organized in a tree structure. Two finite-state approximations to the tree MDP are developed to find near-optimal policies efficiently. Finally, we put forth a nested value iteration algorithm for the finite-state approximations, which is proved to be faster than standard value iteration. Numerical results demonstrate the effectiveness of our methods.

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