Minimal implementation of Proximal Policy Optimization (PPO) in PyTorch

• support discrete and continuous action space
  • In continuous action space, we use the constance std for sampling.
• utils to plot learning graphs in tensorboard

• 2023-09-09
  • Update "Generative Adversarial Imitation Learning(GAIL)"

Find or make a config file and run the following command.

python main.py --config=configs/Ant-v4.yaml 
               --exp_name=test
               --train

python make_expert_dataset.py --experiment_path=checkpoints/Ant/test
                              --load_postfix=last
                              --minimum_score=5000
                              --n_episode=30

python main.py --experiment_path=checkpoints/Ant/test
               --eval
               --eval_n_episode=50
               --load_postfix=last
               --video_path=videos/Ant

• load_path: pretrained model prefix(ex/ number of episode, 'best' or 'last') to play

Environment	Performance Chart	Evaluation Video
Ant-v4		ant.mp4
Ant-v4 (GAIL)		ant_gail.mp4
Reacher-v4		reacher.mp4
HalfCheetah-v4		cheetah.mp4

• IMPLEMENTATION MATTERS IN DEEP POLICY GRADIENTS: A CASE STUDY ON PPO AND TRPO
• https://github.com/junkwhinger/PPO_PyTorch
• https://github.com/nikhilbarhate99/PPO-PyTorch