We’re hiring!

The RLlib team at Anyscale Inc., the company behind Ray, is hiring interns and full-time reinforcement learning engineers to help advance and maintain RLlib. If you have a background in ML/RL and are interested in making RLlib the industry-leading open-source RL library, apply here today. We’d be thrilled to welcome you on the team!

RLlib Table of Contents

RLlib Core Concepts

Troubleshooting

If you encounter errors like blas_thread_init: pthread_create: Resource temporarily unavailable when using many workers, try setting OMP_NUM_THREADS=1. Similarly, check configured system limits with ulimit -a for other resource limit errors.

For debugging unexpected hangs or performance problems, you can run ray stack to dump the stack traces of all Ray workers on the current node, ray timeline to dump a timeline visualization of tasks to a file, and ray memory to list all object references in the cluster.

TensorFlow 2.0

RLlib supports both tf2.x as well as tf.compat.v1 modes. Always use the ray.rllib.utils.framework.try_import_tf() utility function to import tensorflow. It returns three values: * tf1: The tf.compat.v1 module or the installed tf1.x package (if the version is < 2.0). * tf: The installed tensorflow module as-is. * tfv: A convenience version int, whose values are either 1 or 2.

See here for a detailed example script.