Focus of Attention in Sequential Decision Making

Lihong Li, Vadim Bulitko, and Russell Greiner

We investigate the problem of using function approximation in reinforcement learning (RL) where the agent’s control policy is represented as a classifier mapping states to actions. The innovation of this paper lies with introducing a measure of state’s decision-making importance. We then use an efficient approximation to this measure as misclassification costs in learning the agent’s policy. As a result, the focused learning process is shown to converge faster to better policies.

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