Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares policy iteration (LSPI) framework allows us to employ statistical active learning methods for linear regression. Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high. We demonstrate the usefulness of the proposed method, named active policy iteration (API), through simulations with a batting robot.

Authors

• Takayuki Akiyama
• Hirotaka Hachiya Takayuki Akiyama
• Masashi Sugiyama RIKEN Center for Advanced Intelligence Project The University of Tokyo

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