Thompson sampling based Monte-Carlo planning in POMDPs

Abstract

Monte-Carlo tree search (MCTS) has been drawing
great interest in recent years for planning under uncertainty. One of the key challenges is the tradeoff
between exploration and exploitation. To address
this, we introduce a novel online planning algorithm
for large POMDPs using Thompson sampling based
MCTS that balances between cumulative and simple regrets.
The proposed algorithm — Dirichlet-Dirichlet-
NormalGamma based Partially Observable Monte-
Carlo Planning (D2NG-POMCP) — treats the accumulated
reward of performing an action from a belief
state in the MCTS search tree as a random variable following
an unknown distribution with hidden parameters.
Bayesian method is used to model and infer the
posterior distribution of these parameters by choosing
the conjugate prior in the form of a combination of two
Dirichlet and one NormalGamma distributions. Thompson
sampling is exploited to guide the action selection in
the search tree. Experimental results confirmed that our
algorithm outperforms the state-of-the-art approaches
on several common benchmark problems.

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