PPO-Continuous-Pytorch

I found the current implementation of PPO on continuous action space is whether somewhat complicated or not stable.
And this is a clean and robust Pytorch implementation of PPO on continuous action space. Here is the result:

All the experiments are trained with same hyperparameters. Other RL algorithms by Pytorch can be found here.

Dependencies

gym==0.18.3
box2d==2.3.10
numpy==1.21.2
pytorch==1.8.1
tensorboard==2.5.0

How to use my code

Train from scratch

run 'python main.py', where the default enviroment is Pendulum-v0.

Play with trained model

run 'python main.py --write False --render True --Loadmodel True --ModelIdex 400'

Change Enviroment

If you want to train on different enviroments, just run 'python main.py --EnvIdex 0'.
The --EnvIdex can be set to be 0~5, where
'--EnvIdex 0' for 'BipedalWalker-v3'
'--EnvIdex 1' for 'BipedalWalkerHardcore-v3'
'--EnvIdex 2' for 'LunarLanderContinuous-v2'
'--EnvIdex 3' for 'Pendulum-v0'
'--EnvIdex 4' for 'Humanoid-v2'
'--EnvIdex 5' for 'HalfCheetah-v2'

P.S. if you want train on 'Humanoid-v2' or 'HalfCheetah-v2', you need to install MuJoCo first.

Visualize the training curve

You can use the tensorboard to visualize the training curve. History training curve is saved at '\runs'

Hyperparameter Setting

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

References

Proximal Policy Optimization Algorithms
Emergence of Locomotion Behaviours in Rich Environments