Deep Deterministic Policy Gradient

Warning: This repo is no longer maintained. For a more recent (and improved) implementation of DDPG see https://github.com/openai/baselines/tree/master/baselines/ddpg .

Paper: "Continuous control with deep reinforcement learning" - TP Lillicrap, JJ Hunt et al., 2015

Installation

Install Gym and TensorFlow. Then:

pip install pyglet # required for gym rendering
pip install jupyter # required only for visualization (see below)

git clone https://github.com/SimonRamstedt/ddpg.git # get ddpg

Usage

Example:

python run.py --outdir ../ddpg-results/experiment1 --env InvertedDoublePendulum-v1

Enter python run.py -h to get a complete overview.

If you want to run in the cloud or a university cluster this might contain additional information.

Visualization

Example:

python dashboard.py --exdir ../ddpg-results/+

Enter python dashboard.py -h to get a complete overview.

Known issues

• No batch normalization yet
• No conv nets yet (i.e. only learning from low dimensional states)
• No proper seeding for reproducibilty

Please write me or open a github issue if you encounter problems! Contributions are welcome!

Improvements beyond the original paper

• Output normalization – the main reason for divergence are variations in return scales. Output normalization would probably solve this.
• Prioritized experience replay – faster learning, better performance especially with sparse rewards – Please write if you have/know of an implementation!

Advaned Usage

Remote execution:

python run.py --outdir your_username@remotehost.edu:/some/remote/directory/+ --env InvertedDoublePendulum-v1