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README.md

Roboschool agents training with Ray and TensorFlow on Amazon SageMaker

Roboschool is an open source physics simulator that is commonly used to train RL policies for robotic systems. Roboschool defines a variety of Gym environments that correspond to different robotics problems. Here we're highlighting a few of them at varying levels of difficulty:

  • Reacher (easy) - a very simple robot with just 2 joints reaches for a target
  • Hopper (medium) - a simple robot with one leg and a foot learns to hop down a track
  • Humanoid (difficult) - a complex 3D robot with two arms, two legs, etc. learns to balance without falling over and then to run on a track

The simpler problems train faster with less computational resources. The more complex problems are more fun.

In these examples, we demonstrate:

  • Vertical scaling of RL training (single node, multiple CPU cores or GPUs)
  • Horizontal scaling of RL training across multiple nodes (CPU or GPU)
  • Use of SageMaker's Automatic Model Tuning functionality to optimize the training of an RL model, using the Roboschool environment.

Contents

  • rl_roboschool_ray.ipynb: Scaling RL training across multiple CPU cores (vertical scaling)
  • rl_roboschool_ray_automatic_model_tuning.ipynb: Shows how to use SageMaker's Automatic Model Tuner to optimize hyperparameters
  • rl_roboschool_ray_distributed.ipynb: Scaling RL training across multiple instances, including heterogeneous GPU and CPU clusters
  • Dockerfile: Dockerfile building the container with Roboschool, Ray and their dependencies by using SageMaker's RL tensorflow container as base.
  • src/
    • train-hopper.py: PPO config for training RoboschoolHopper-v1
    • train-humanoid.py: PPO config for training RoboschoolHumanoid-v1
    • train-reacher.py: PPO config for training RoboschoolReacher-v1