Rollout Heuristics for Online Stochastic Contingent Planning
This work addresses the need for domain-independent heuristics in POMCP for practitioners in planning and decision-making under uncertainty, but it is incremental as it adapts existing planning heuristics.
The paper tackles the problem of POMCP's reliance on domain-specific heuristics by modeling POMDPs as stochastic contingent planning problems, resulting in the proposal of two domain-independent heuristics based on h_add and belief space with value of information.
Partially observable Markov decision processes (POMDP) are a useful model for decision-making under partial observability and stochastic actions. Partially Observable Monte-Carlo Planning is an online algorithm for deciding on the next action to perform, using a Monte-Carlo tree search approach, based on the UCT (UCB applied to trees) algorithm for fully observable Markov-decision processes. POMCP develops an action-observation tree, and at the leaves, uses a rollout policy to provide a value estimate for the leaf. As such, POMCP is highly dependent on the rollout policy to compute good estimates, and hence identify good actions. Thus, many practitioners who use POMCP are required to create strong, domain-specific heuristics. In this paper, we model POMDPs as stochastic contingent planning problems. This allows us to leverage domain-independent heuristics that were developed in the planning community. We suggest two heuristics, the first is based on the well-known h_add heuristic from classical planning, and the second is computed in belief space, taking the value of information into account.