Customisable Control Policy Learning for Robotics

Deep reinforcement learning algorithms integratedeep neural networks with traditional reinforcement learningmethodologies. These techniques have been developed and usedfor various applications to produce exciting results in manyfields, including robotics. However, physical robots require alarge amount of training episodes which can damage the robotif directed by immature policies. Training using simulations canserve as a viable alternative before a robot is deployed in thefield. This study addresses a computational challenge of deepreinforcement learning by developing a hardware architecturefor the Deep Deterministic Policy Gradient (DDPG) algorithm.Additionally, we identify the customisation opportunities for afull-stack development framework with reinforcement learningto discover control policies for robotic arms. Finally, we transferpolicies encoded in fixed-point numbers from our FPGA DDPGimplementation to a robotic arm to evaluate the feasibility of ourlearning platform