Proposal of a Deep Q-network with Profit Sharing
Kazuteru Miyazaki · Procedia Computer Science · 2018
Deep learning has attracted significant interest currently. The deep Q-network (DQN) combined with Q-learning have demonstrated 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 a DQN and PS. The proposed method DQNwithPS is compared to a DQN in Pong of Atari 2600 games. We demonstrate that the proposed DQNwithPS method can learn stably with fewer trial-and-error searches than only using a DQN.