JACIII Vol.21 No.5 pp. 849-855
doi: 10.20965/jaciii.2017.p0849


Exploitation-Oriented Learning with Deep Learning – Introducing Profit Sharing to a Deep Q-Network –

Kazuteru Miyazaki

National Institution for Academic Degrees and Quality Enhancement of Higher Education
1-29-1 Gakuennishimachi, Kodaira, Tokyo 185-8587, Japan

March 21, 2017
July 21, 2017
September 20, 2017
reinforcement learning, deep learning, deep reinforcement learning, profit sharing, deep Q-network

Currently, deep learning is attracting significant interest. Combining deep Q-networks (DQNs) and Q-learning has produced excellent results for several Atari 2600 games. In this paper, we propose an exploitation-oriented learning (XoL) method that incorporates deep learning to reduce the number of trial-and-error searches. We focus on a profit sharing (PS) method that is an XoL method, and combine it with a DQN to propose a DQNwithPS method. This method is compared with a DQN in Atari 2600 games. We demonstrate that the proposed DQNwithPS method can learn stably with fewer trial-and-error searches than required by only a DQN.

Cite this article as:
K. Miyazaki, “Exploitation-Oriented Learning with Deep Learning – Introducing Profit Sharing to a Deep Q-Network –,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.5, pp. 849-855, 2017.
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