RLlib

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 Policy Gradients (PG).

1. rllib/pg_example.py This example shows the basics of using RLlib with SMARTS through RLlibHiWayEnv. 1. rllib/pg_pbt_example.py This example combines Policy Gradients with Population Based Training (PBT) scheduling.

Recommended reads

RLlib is implemented on top of Ray. Ray is a distributed computing framework specifically designed with RL in mind. There are many docs about Ray and RLlib. We recommend to read the following pages first,

• RLlib in 60 seconds: Getting started with RLlib.

• Common Parameters: Common tune configs.

• Basic Python API: Basic tune.run function.

• Callbacks and Custom Metrics: About callbacks and metrics.

• Visualizing Custom Metrics: How to use TensorBoard to visualize metrics.

• Built-in Models and Preprocessors: Built-in preprocessor, including how to deal with different observation spaces.

• Proximal Policy Optimization (PPO): RLlib PPO implementation and PPO parameters.

• PopulationBasedTraining: Population Based Training algorithm and examples.

• RLlib Examples: Get to know RLlib quickly through examples.

Resume training

With respect to SMARTS/examples/rl/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.