tensorflow-rl-pong

Reinforcement Learning Pong on Cloud Machine Learning Engine

Introduction

This is a code sample for the talk "Tensorflow and deep reinforcement learning, without a PhD" (video | slides)

Author: Yu-Han Liu

Prerequisites

• Python 2.7 or Python 3
• Google Cloud SDK

Installation

pip install -r requirements.txt

To verify that the packages are installed successfully, run the following command to see a round of Pong gameplay rendered on screen:

python -m trainer.task --render

This package comes with a trained checkpoint. To play a game with it, run:

python -m trainer.task --render --restore --output-dir ./demo-checkpoint

You can also run the following command to train locally. By default, outputs (checkpoints and TensorBoard summaries) are written to /tmp/pong_output:

python -m trainer.task

Run training job

To submit the training job to Cloud Machine Learning Engine:

GCS_BUCKET=<gs://YOUR-BUCKET/> bash submit.sh

Monitor training job

1. You can monitor the training job and find the logs in the cloud console.

2. To monitor the progress of training, use TensorBoard:

   tensorboard --logdir gs://YOUR-BUCKET/JOB_NAME
   

   Here JOB_NAME is of the format of "pong_[timestamp]", which you can find on the console.

3. To watch Pong gameplay with a trained model, run:

   python -m trainer.task --output-dir gs://YOUR-BUCKET/JOB_NAME --restore --render
   

   It might take about 10 minutes before the first checkpoint is saved, and it might take a few hours before the trained model can consistently win a some points. It might take about 20 hours of training before the trained model can consistently win games.

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Disclaimer: This is not an official Google product but sample code provided for an educational purpose