Pytorch-DPPO

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Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Pytorch-DPPO 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# 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](https://raw.githubusercontent.com/alexis-jacq/Pytorch-DPPO/master/figs/invertpendulum.png)

InvertedDoublePendulum

![InvertedDoublePendulum](https://raw.githubusercontent.com/alexis-jacq/Pytorch-DPPO/master/figs/inverteddouble.png)

HalfCheetah

![HalfCheetah](https://raw.githubusercontent.com/alexis-jacq/Pytorch-DPPO/master/figs/halfcheetah.png)

hopper (PyBullet)

![hopper (PyBullet)](https://raw.githubusercontent.com/alexis-jacq/Pytorch-DPPO/master/figs/hopper_bullet.png)

halfcheetah (PyBullet)

![halfcheetah (PyBullet)](https://raw.githubusercontent.com/alexis-jacq/Pytorch-DPPO/master/figs/halfcheetah_bullet.png)

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

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