AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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TD-DeltaPi: A Model-Free Algorithm for Efficient Exploration
Bruno C. da Silva, Andrew G. Barto

Last modified: 2012-07-14


We study the problem of finding efficient exploration policies for the case in which an agent is momentarily not concerned with exploiting, and instead tries to compute a policy for later use. We first formally define the Optimal Exploration Problem as one of sequential sampling and show that its solutions correspond to paths of minimum expected length in the space of policies. We derive a model-free, local linear approximation to such solutions and use it to construct efficient exploration policies. We compare our model-free approach to other exploration techniques, including one with the best known PAC bounds, and show that ours is both based on a well-defined optimization problem and empirically efficient.


reinforcement learning; exploration; markov process; pac-mdp; control

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