REACT Lab presents new work on sequential decision making for POMDP at the MIT Learning for Decision and Control workshop in May 2019

May 1, 2019
Sensor information from wireless signals used for multi-robot PGO.

Reinforcement Learning for POMDP: Rollout and Policy Iteration with Application to Sequential Repair

Sushmita Bhattacharya, Thomas Wheeler
advised by Stephanie Gil, Dimitri P. Bertsekas


We study rollout algorithms which combine limited lookahead and terminal cost function approximation in the context of POMDP. We demonstrate their effectiveness in the context of a sequential pipeline repair problem, which also arises in other contexts of search and rescue. We provide performance bounds and empirical validation of the methodology, in both cases of a single rollout iteration, and multiple iterations with intermediate policy space approximations.

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