Skip to content

DDPG algorithm on the OpenAI Humanoidv2 environment

Notifications You must be signed in to change notification settings

mokeam/DDPG-Humanoid

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DDPG Humanoid

We conducted experiments of the performance of DDPG algorithm on the OpenAI Humanoidv2 environment based on what we learned from Lillicrap et al. (2016) and Plappert et al. (2018). Lillicrap et al. (2016) is about the DDPG algorithm, with noise, sampled with the OrnsteinUhlenbeck process, in the action space for exploration, while Plappert et al. (2018) is about possible improvements with noise in the parameter space.

With the experiments we conducted, we saw that the DDPG algorithm without noise is by far the best performer in learning policies for the OpenAI gym Humanoid-v2 environment.

Demo

Experiment Report

https://github.com/mokeam/DDPG-Humanoid/blob/master/garba_makinwa_ddpg_humanoid_report.pdf

About

DDPG algorithm on the OpenAI Humanoidv2 environment

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages