Tree Exploration for Bayesian RL Exploration

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Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The ﬁrst employs a Bayesian framework, where optimality improves with increased computational time. This is because the resulting planning task takes the form of a dynamic programming problem on a belief tree with an inﬁnite number of states. The second type employs relatively simple algorithm which are shown to suffer small regret within a distribution-free framework. This paper presents a lower bound and a high probability upper bound on the optimal value function for the nodes in the Bayesian belief tree, which are analogous to similar bounds in POMDPs. The bounds are then used to create more efﬁcient strategies for exploring the tree. The resulting algorithms are compared with the distribution-free algorithm UCB1, as well as a simpler baseline algorithm on multiarmed bandit problems.

Christos Dimitrakakis

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Algorithm | Bayesian Belief Tree | Belief Tree | CIMCA 2008 | Internet Technology |

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Added	29 May 2010
Updated	15 Dec 2011
Type	Conference
Year	2008
Where	CIMCA
Authors	Christos Dimitrakakis

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Tree Exploration for Bayesian RL Exploration

Algorithm | Bayesian Belief Tree | Belief Tree | CIMCA 2008 | Internet Technology |

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