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Thompson sampling based Monte-Carlo planning in POMDPs

Record type: Conference or Workshop Item (Paper)

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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Citation

Bai, Aijun, Wu, Feng, Zhang, Zongzhang and Chen, Xiaoping (2014) Thompson sampling based Monte-Carlo planning in POMDPs At Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS-14), United States. 21 - 26 Jun 2014.

More information

Accepted/In Press date: June 2014
Venue - Dates: Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS-14), United States, 2014-06-21 - 2014-06-26
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 360985
URI: http://eprints.soton.ac.uk/id/eprint/360985
PURE UUID: 1054b540-c04e-4424-833d-a2949c9e365f

Catalogue record

Date deposited: 14 Jan 2014 10:39
Last modified: 18 Jul 2017 03:08

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Contributors

Author: Aijun Bai
Author: Feng Wu
Author: Zongzhang Zhang
Author: Xiaoping Chen

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