Distributed-RL

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Distributed DRL by Ray and TensorFlow Tutorial.

Distributed-RL 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Distributed Deep Reinforcement Learning

Distributed Deep Reinforcement Learning by Ray and TensorFlow.

This framework inspired by general-purpose RL training system **Rapid** from OpenAI.

Rapid framework:
![rapid-architecture@2x--1-](https://raw.githubusercontent.com/LiuShuai26/Distributed-RL/master/tutorial/Pictures/rapid-architecture@2x--1-.png)
This framework:
![ddrlframework](https://raw.githubusercontent.com/LiuShuai26/Distributed-RL/master/tutorial/Pictures/ddrlframework.jpg)

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Tutorial (Chinese version)

- [Parallelize your algorithm by Ray (1)](tutorial/Parallelize%20your%20algorithm%20by%20Ray%20(1).md)
- [Parallelize your algorithm by Ray (2)](tutorial/Parallelize%20your%20algorithm%20by%20Ray%20(2).md)
- [Parallelize your algorithm by Ray (3)](tutorial/Parallelize%20your%20algorithm%20by%20Ray%20(3).md)

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In short. This framework divides the reinforcement learning process into five parts:

- Replay buffer (option)
- Parameter server
- train (learn)
- rollout
- test

简单实验对比：

实验：LunarLanderContinuous-v2

算法：SAC

未调参，sac和dsac参数相同，dsac的worker数量：1。GPU：GTX1060

(dsac: distributed sac)

![dsac1w-sac](https://raw.githubusercontent.com/LiuShuai26/Distributed-RL/master/tutorial/Pictures/dsac1w-sac.png)

Distributed-RL

Distributed-RL 최신버젼 다운로드

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