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

In this article, we learn to vectorize an RL environment and train 30 Q-learning agents in parallel on a CPU, at 1.8 million iterations per second.

In the previous story, we introduced Temporal-Difference Learning, particularly Q-learning, in the context of a GridWorld.

Temporal-Difference Learning and the importance of exploration: An illustrated guide

While this implementation served the purpose of demonstrating the differences in performances and exploration mechanisms of these algorithms, it was painfully …

jax machine learning parallel-computing python reinforcement learning