# A3CSuperMario_Windows

**Repository Path**: fuwujia/A3CSuperMario_Windows

## Basic Information

- **Project Name**: A3CSuperMario_Windows
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-08-29
- **Last Updated**: 2021-08-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# A3CSuperMario_Windows
## result

![alt text](https://raw.githubusercontent.com/xushsh163/A3CSuperMario_Windows/master/result.gif)

3 threads

after 4000 episodes, about 30% chance of Clearance

after 8000 episodes, about 70% chance of Clearance

## Enviroment

Install NES emulator [FCEUX](http://www.fceux.com/web/home.html)

lua scripts and nes files in 'SuperMarioBros' Folder are modified from [gym-super-mario](https://github.com/ppaquette/gym-super-mario)

Install pywin32

using win32 named pipe to comunicate with FCEUX, so support winodws only

## Config

> config.py
```
FCEUX_PATH = 'D:\\fceux-2.2.3-win32\\fceux.exe'
```
change to your install path

> A3CTrainer.py
```
load_model = False
```
change to True if you need to continue training

## Run

Train

```
python A3CTrainer.py
```

Test

```
python A3CTester.py
```


## A3C model

[introduction article to this impelement](https://zhuanlan.zhihu.com/p/53907806)

A3C model implements is from this article 《[simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-8-asynchronous-actor-critic-agents-a3c-c88f72a5e9f2)》.

I have tried a lot of A3C implements and this one is most efficient.