Skip to content
/ ED2 Public

Official repository for: Continuous Control With Ensemble DeepDeterministic Policy Gradients

License

Notifications You must be signed in to change notification settings

ed2-paper/ED2

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Continuous Control With Ensemble Deep Deterministic Policy Gradients

This repository is the official implementation of Continuous Control With Ensemble Deep Deterministic Policy Gradients.

image

Requirements

Before installation, please make sure you have MuJoCo engine set up on your machine. We use mujoco150 in order to be comparable with previous benchmarks on v2 environments. See this issue

To install requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper, run this command:

python run.py <experiment_specification path>

Logger automatically stops training and evaluates current policy every log_every environment interactions. The data is printed to standard output and stored on drive.

We include specifications for our most important experiments.

Path Description
specs/ed2_on_mujoco.py Benchmark of our method
specs/sac_on_mujoco.py Benchmark of our implementation of SAC
specs/sunrise_on_mujoco.py Benchmark of our implementation of SUNRISE
specc/sop_on_mujoco.py Benchmark of our implementation of SOP

Results

Our model achieves the following performance on the MuJoCo suite:

image

About

Official repository for: Continuous Control With Ensemble DeepDeterministic Policy Gradients

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages