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Implementation of Deep Deterministic Policy Gradients using TensorFlow, compatible with the OpenAI Gym

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README.md

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dashboard.ipynb

dashboard.ipynb

 
 

dashboard.py

dashboard.py

 
 

ddpg.py

ddpg.py

 
 

ddpg_nets_dm.py

ddpg_nets_dm.py

 
 

experiment.py

experiment.py

 
 

filter_env.py

filter_env.py

 
 

replay_memory.py

replay_memory.py

 
 

run.py

run.py

 
 

testrun

testrun

 
 

util.py

util.py

 
 

visualization.py

visualization.py

 
 

visualize.ipynb

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Deep Deterministic Policy Gradient

Paper: Continuous control with deep reinforcement learning

This is a only a preview, a proper version will be released soon

Installation

pip install pyglet # for gym rendering
pip install jupyter # for visualization (optional)

Usage

Example:

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

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

Visualization

Example:

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

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

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Implementation of Deep Deterministic Policy Gradients using TensorFlow, compatible with the OpenAI Gym

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