Vectorize and Parallelize RL Environments with JAX: Q-learning at the Speed of Light⚡
In the previous story, we introduced Temporal-Difference Learning, particularly Q-learning, in the context of a GridWorld.
While this implementation served the purpose of demonstrating the differences in performances and exploration mechanisms of these algorithms, it was painfully slow.
Indeed, the environment and agents were mainly coded in Numpy, which is by no means a standard in RL, even though it makes the code easy to understand and debug.
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:While this implementation served the purpose of demonstrating the differences in performances and exploration mechanisms of these algorithms, it was painfully slow.
Indeed, the environment and agents were mainly coded in Numpy, which is by no means a standard in RL, even though it makes the code easy to understand and debug.
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
JAX is yet another Python Deep Learning framework developed by Google and widely used by companies such as DeepMind.
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