Multiple-Environment Markov Decision Processes: Efficient Analysis and Applications

Krishnendu Chatterjee, Martin Chmelík, Deep Karkhanis, Petr Novotný, Amélie Royer

PosterID: 62
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Multiple-environment Markov decision processes (MEMDPs) are MDPs equipped with not one, but multiple probabilistic transition functions, which represent the various possible unknown environments. While the previous research on MEMDPs focused on theoretical properties for long-run average payoff, we study them with discounted-sum payoff and focus on their practical advantages and applications. MEMDPs can be viewed as a special case of Partially observable and Mixed observability MDPs: the state of the system is perfectly observable, but not the environment. We show that the specific structure of MEMDPs allows for more efficient algorithmic analysis, in particular for faster belief updates. We experimentally demonstrate the applicability of MEMDPs in several domains, including contextual recommendation systems and parameterized Markov decision processes.

Session E3: Planning with Uncertainty
Canb 10/29/2020, 01:00 – 02:00
10/30/2020, 21:00 – 22:00
Paris 10/28/2020, 15:00 – 16:00
10/30/2020, 11:00 – 12:00
NYC 10/28/2020, 10:00 – 11:00
10/30/2020, 06:00 – 07:00
LA 10/28/2020, 07:00 – 08:00
10/30/2020, 03:00 – 04:00