RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications. RLlib natively supports TensorFlow, TensorFlow Eager, and PyTorch. Most of its internals are agnostic to such deep learning frameworks.

SMARTS contains two examples using Proximal Policy Optimization (PPO).

  1. Proximal policy optimization

  2. Proximal policy optimization with population based training

Resume training

With respect to SMARTS/examples/e12_rllib examples, if you want to continue an aborted experiment, you can set resume_training=True. But note that resume_training=True will continue to use the same configuration as was set in the original experiment. To make changes to a started experiment, you can edit the latest experiment file in ./results.

Or if you want to start a new experiment but train from an existing checkpoint, you will need to look into How to Save and Load Trial Checkpoints.