Vectorize and Parallelize RL Environments with JAX: Q-learning at the Speed of Light⚡

Summary

In this article, we’ll see how to scale up RL experiments by vectorizing environments and seamlessly parallelizing the training of dozens of agents using JAX. In particular, this article covers:

• JAX basics and useful features for RL
• Vectorized environment and why they are so fast
• Implementation of an environment, policy, and Q-learning agent in JAX
• Single-agent training
• How to parallelize agent training, and how easy it is!

Read the full article on Towards Data Science!