Distributed deep reinforcement learning on the cloud for autonomous driving
Mitchell Spryn, Aditya Jaikumar Sharma, Dhawal Parkar, Madhur Shrimal · 2018
This paper proposes an architecture for leveraging cloud computing technology to reduce training time for deep reinforcement learning models for autonomous driving by distributing the training process across a pool of virtual machines. By parallelizing the training process, careful design of the reward function and use of techniques like transfer learning, we demonstrate a decrease in training time for our example autonomous driving problem from 140 hours to less than 1 hour. We go over our network architecture, job distribution paradigm, reward function design and report results from experiments on small sized cluster (1--6 training nodes) of machines. We also discuss the limitations of our approach when trying to scale up to massive clusters.