Parallelization of evolution of reinforcement learning agents using GPGPU

Abstract

GPGPU is a parallel computation technology using GPU that has huge number of processor cores for parallelly calculating colors of pixels on a monitor. In a previous work, we used GPGPU to parallelize many runs of reinforcement learning agents for calculating their tness in a simulation of evolution. It speeded up the simulation surprisingly. However, the evolution part was sequentially run in CPU and the communication between CPU and GPU happened in every generation. Hence, this work uses GPGPU to parallelize the evolution part in addition to the tness calculation. It makes the simulation even faster due to parallelism and the reduction of latency between CPU and GPU.