RLlib: Scalable Reinforcement Learning¶
RLlib is an open-source library for reinforcement learning that offers both a collection of reference algorithms and scalable primitives for composing new ones.
pip install tensorflow # or tensorflow-gpu pip install ray[rllib] # also recommended: ray[debug]
You might also want to clone the Ray repo for convenient access to RLlib helper scripts:
git clone https://github.com/ray-project/ray cd ray/python/ray/rllib
- High-throughput architectures
- Multi-agent specific
Models and Preprocessors¶
You can find an index of RLlib code examples on this page. This includes tuned hyperparameters, demo scripts on how to use specific features of RLlib, and several community examples of applications built on RLlib.
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.
If you encounter out-of-memory errors, consider setting
ray.init() to reduce memory usage.
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, and
ray timeline to dump
a timeline visualization of tasks to a file.