Pytorch implementation of Distributed Proximal Policy Optimization

Pytorch-DPPO

Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https://arxiv.org/pdf/1707.06347.pdf).

I finally fixed what was wrong with the gradient descent step, using previous log-prob from rollout batches. At least ppo.py is fixed, the rest is going to be corrected as well very soon.

In the following example I was not patient enough to wait for million iterations, I just wanted to check if the model is properly learning:

Progress of single PPO:

InvertedPendulum

InvertedPendulum

InvertedDoublePendulum

InvertedDoublePendulum

HalfCheetah

HalfCheetah

hopper (PyBullet)

hopper (PyBullet)

halfcheetah (PyBullet)

halfcheetah (PyBullet)

Progress of DPPO (4 agents) [TODO]

Acknowledgments

The structure of this code is based on https://github.com/ikostrikov/pytorch-a3c.

Hyperparameters and loss computation has been taken from https://github.com/openai/baselines


Download Details:

Author: Alexis-jacq
Source Code: https://github.com/alexis-jacq/Pytorch-DPPO 
License: MIT license

#machinelearning #python #pytorch 

Pytorch implementation of Distributed Proximal Policy Optimization
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