LOW-COST FIELD PROGRAMMABLE GATE ARRAY ACCELERATES DEEP Q-LEARNING

Abstract. Due to recent advances in digital technologies, deep reinforcement learning has emerged, and has demonstrated its ability and effectiveness in solving complex learning problems not possible before. In particular, convolution neural networks (CNNs) have been demonstrated their effectiveness in reinforcement learning. However, they require intensive CPU operations and memory bandwidth that make general CPUs fail to achieve desired performance levels. In this paper, we used some low-cost field programming gates array (FPGA) designed a parallel Deep Qlearning accelerator to solve this problem. And the system has high efficient and flexibility.