Learning in parallel

Reinforcement learning is time-consuming for complex tasks due to the need for large amounts of training data. Recent advances in GPU-based simulation, such as Isaac Gym, have sped up data collection thousands of times on a commodity GPU. Most prior works ...

With respect to the problem of the slow convergence of the traditional reinforcement learning algorithm in practical applications, we propose a novel multi-threading parallel reinforcement learning algorithm - MPRL. MPRL is mainly composed of two parts. ...

A simple program that approximates pi by numerical quadrature is rewritten to run on nine commercially available processors to illustrate the compilations that arise in parallel programming in FORTRAN. The machines used are the Alliant FX/8, BBN ...