junkwhinger / PPO_PyTorch

This repo contains PPO implementation in PyTorch for LunarLander-v2

PPO Implementation in Pytorch for LunarLander-v2

Being fastinated by "IMPLEMENTATION MATTERS IN DEEP POLICY GRADIENTS: A CASE STUDY ON PPO AND TRPO", I wrote PPO code in PyTorch to see if the code-level optimizations work for LunarLander-v2. And they do! for some extent.

How to train

Find a config .yaml file in the config directory and run the following command. You can make your own .yaml file, but make sure they have all the necessary options.

$ python main.py --config PPO_M.yaml

How to play

$ python main.py --config PPO_M.yaml --eval

How to run Bayesian Optimization for hyperparameters

$ python search.py

Reference

• IMPLEMENTATION MATTERS IN DEEP POLICY GRADIENTS: A CASE STUDY ON PPO AND TRPO
• https://github.com/nikhilbarhate99/PPO-PyTorch/blob/master/PPO.py
• https://github.com/mimoralea/gdrl/blob/master/notebooks/chapter_12/chapter-12.ipynb
• https://github.com/reinforcement-learning-kr/pg_travel/blob/master/mujoco/agent/ppo_gae.py
• https://github.com/openai/spinningup/tree/master/spinup/algos/pytorch/ppo
• https://github.com/openai/baselines/tree/master/baselines/ppo2
• https://github.com/hill-a/stable-baselines/tree/master/stable_baselines/ppo1
• https://medium.com/@jonathan_hui/rl-proximal-policy-optimization-ppo-explained-77f014ec3f12