Scalable Reinforcement Learning Using Azure ML and Ray

Accelerate deep RL training on custom gym environments using RLLIB on Azure ML Cluster.

Single-machine and single-agent RL training have many challenges, the most important being the time it takes for the rewards to converge. Most of the time spent by the agent in RL training goes into gathering experiences. The time taken for simple applications is a few hours, and complex applications take days. Deep Learning frameworks like Tensorflow support distributed training; can the same be applied to RL …

azure deep learning distributed systems learning machine learning ml ray reinforcement reinforcement learning rllib scalable