Customisable Control Policy Learning for Robotics

Abstract

Deep reinforcement learning algorithms integrate deep neural networks with traditional reinforcement learning methodologies. These techniques have been developed and used for various applications to produce exciting results in many fields, including robotics. However, physical robots require a large amount of training episodes which can damage the robot if directed by immature policies. Training using simulations can serve as a viable alternative before a robot is deployed in the field. This study addresses a computational challenge of deep reinforcement learning by developing a hardware architecture for the Deep Deterministic Policy Gradient (DDPG) algorithm. Additionally, we identify the customisation opportunities for a full-stack development framework with reinforcement learning to discover control policies for robotic arms. Finally, we transfer policies encoded in fixed-point numbers from our FPGA DDPG implementation to a robotic arm to evaluate the feasibility of our learning platform.