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¶
Models, Preprocessors, and Action Distributions¶
Model-based / Meta-learning / Offline
Exploration-based plug-ins (can be combined with any algo)
Concepts and Custom Algorithms¶
If you encounter errors like
blas_thread_init: pthread_create: Resource temporarily unavailable when using many workers,
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.
RLlib supports both tf2.x as well as
Always use the
ray.rllib.utils.framework.try_import_tf() utility function to import tensorflow.
It returns three values:
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.