PPO-Discrete-Pytorch

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PPO-Discrete-Pytorch

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A clean and robust Pytorch implementation of PPO on Discrete action space

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최종 버전 다운로드 (.zip)

- ppo_actor300000.pth
- ppo_critic300000.pth
- - events.out.tfevents.1636443992.DESKTOP-7RSSKLJ.17936.0
- - events.out.tfevents.1636442428.DESKTOP-7RSSKLJ.21204.0
LICENSE
main.py
Old-Vesion-with-gym==0.18.3.zip
PPO.py
README.md
result.jpg
utils.py

// repository documentation

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# PPO-Discrete-Pytorch
This is a **clean and robust Pytorch implementation of PPO on Discrete action space**. Here is the result:

<img src="https://github.com/XinJingHao/DQN-DDQN-Pytorch/blob/main/IMGs/Render_CVP1.gif" width="90%" height="auto">  | <img src="https://github.com/XinJingHao/DQN-DDQN-Pytorch/blob/main/IMGs/Render%20of%20DDQN.gif" width="90%" height="auto">
:-----------------------:|:-----------------------:|

All the experiments are trained with same hyperparameters. **Other RL algorithms by Pytorch can be found [here](https://github.com/XinJingHao/RL-Algorithms-by-Pytorch).**

## Dependencies
```python
gymnasium==0.29.1
numpy==1.26.1
pytorch==2.1.0

python==3.11.5
```

## How to use my code
### Train from scratch
```bash
python main.py
```
where the default enviroment is 'CartPole'.

### Play with trained model
```bash
python main.py --EnvIdex 0 --render True --Loadmodel True --ModelIdex 300000
```
which will render the 'CartPole'.

### Change Enviroment
If you want to train on different enviroments
```bash
python main.py --EnvIdex 1
```
The --EnvIdex can be set to be 0 and 1, where   
```bash
'--EnvIdex 0' for 'CartPole-v1'  
'--EnvIdex 1' for 'LunarLander-v2'   
```
Note: if you want train on LunarLander-v2, you need to install [box2d-py](https://gymnasium.farama.org/environments/box2d/) first. You can install box2d-py via:
```bash
pip install gymnasium[box2d]
```

### Visualize the training curve
You can use the [tensorboard](https://pytorch.org/docs/stable/tensorboard.html) to record anv visualize the training curve.

- Installation (please make sure PyTorch is installed already):
```bash
pip install tensorboard
pip install packaging
```
- Record (the training curves will be saved at '**\runs**'):
```bash
python main.py --write True
```

- Visualization:
```bash
tensorboard --logdir runs
```

### Hyperparameter Setting
For more details of Hyperparameter Setting, please check 'main.py'

### References
[Proximal Policy Optimization Algorithms](https://arxiv.org/pdf/1707.06347.pdf)  
[Emergence of Locomotion Behaviours in Rich Environments](https://arxiv.org/pdf/1707.02286.pdf)

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