Awesome

Introduction

This repository implements NIPS 2017 Value Prediction Network (Oh et al.) in Tensorflow.

@inproceedings{Oh2017VPN,
  title={Value Prediction Network},
  author={Junhyuk Oh and Satinder Singh and Honglak Lee},
  booktitle={NIPS},
  year={2017}
}

Our code is based on OpenAI's A3C implemenation.

Dependencies

• Tensorflow
• Beutiful Soup
• Golang
• six (for py2/3 compatibility)
• tmux (the start script opens up a tmux session with multiple windows)
• htop (shown in one of the tmux windows)
• gym
• gym[atari]
• universe
• opencv-python
• numpy
• scipy

Training

The following command trains a value prediction network (VPN) with plan depth of 3 on stochastic Collect domain:

python train.py --config config/collect_deterministic.xml --branch 4,4,4 --alg VPN

train_vpn script contains commands for reproducing the main result of the paper.

Notes

• Tensorboard shows the performance of the epsilon-greedy policy. This is NOT the learning curve in the paper, because epsilon decreases from 1.0 to 0.05 for the first 1e6 steps. Instead, [logdir]/eval.csv shows the performance of the agent using greedy-policy.
• Our code supports multi-gpu training. You can specify GPU IDs in --gpu option (e.g., --gpu 0,1,2,3).