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curiosity-driven-exploration-pytorch

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Curiosity-driven Exploration by Self-supervised Prediction

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# Curiosity-driven Exploration by Self-supervised Prediction

- [x] Advantage Actor critic [[1]](#references)
- [x] Parallel Advantage Actor critic [[2]](#references)
- [x] Curiosity-driven Exploration by Self-supervised Prediction [[3]](#references) [[5]](#references)
- [x] Proximal Policy Optimization Algorithms [[4]](#references)

## 1. Setup
####  Requirements

------------

- python3.6
- gym
- [OpenCV Python](https://pypi.python.org/pypi/opencv-python)
- [PyTorch](http://pytorch.org/)
- [tensorboardX](https://github.com/lanpa/tensorboardX)

## 2. How to Train
Modify the parameters in `config.conf` as you like.
```
python train.py
```

## 3. How to Eval
```
python eval.py
```

## 4. Loss/Reward Graph
- Breakout Env
![image](https://user-images.githubusercontent.com/23333028/49057720-29ee9680-f244-11e8-865a-4ccfb360ddd6.png)

References
----------

[1] [Actor-Critic Algorithms](https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf)    
[2] [Efficient Parallel Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1705.04862)  
[3] [Curiosity-driven Exploration by Self-supervised Prediction](https://arxiv.org/abs/1705.05363)   
[4] [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347)  
[5] [Large-Scale Study of Curiosity-Driven Learning](https://arxiv.org/abs/1808.04355)

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