# PPO-discrete + RNN
This is a concise Pytorch implementation of PPO+RNN(GRU/LSTM) on discrete action space.<br />


## How to use my code?
You can dircetly run 'PPO_discrete_rnn_main.py' in your own IDE.<br />

## Trainning environments
You can set the 'env_index' in the codes to change the environments. Here, we train our code in 2 environments.<br />
env_index=0 represent 'CartPole-v1'<br />
env_index=1 represent 'LunarLander-v2'<br />

## Training result
![image](https://github.com/Lizhi-sjtu/DRL-code-pytorch/blob/main/9.PPO-discrete-RNN/PPO%2BRNN.png)

