Implementation of Deep Reinforcement Learning

Reinforcement Learning (RL) is different from supervised learning, which is learning from a training set of labeled examples provided by a knowledgable external supervisor. RL is also different from unsupervised learning, which is typically about finding structure hidden in collections of unlabeled data. A Deep-Q-Network (DQN) RL system relied heavily on GPUs to accelerate computation. However, it is challenging to implement and deploy an RL model in an embedded system which has limited computing units and programming capacity. PYNQ with CPU-FPGA heterogeneous architecture is a platform that aims at developing embedded systems based on FPGA. This paper aims at constructing a fast FPGA prototyping framework for Cart-Pole problem on PYNQ platform.