This is a classic example of a problem that reinforcement learning can solve. It's a simulation of balancing a broom upright by balancing it on your hand. The broom is the "pole" and your hand is replaced with a "cart" moving back and forth on a linear track. This simplified example works in 2 dimensions, so the cart can only move in a line back and forth, and the pole can only fall forwards or backwards, not to the sides.
Sample Cart Pole Example using SageMaker RL with base docker image containing Coach, MxNet and OpenAI Gym. This is a toy example taken from
Coach Quick Start Guide. It demonstrates how you can use the RLEstimator
from the SageMaker Python SDK in script
mode.
The sample notebook demonstrates how to:
- Train a toy cart pole model from a notebook and python SDK/script mode
- Visualize
gifs
generated during training. - Move these back and forth from S3 (while running in
SageMaker
mode) - Checkpoint trained model
- Run inference using checkpointed models