PPO_in_10min

PPO code - All you need to know (80/20 rule applied)

Bioengineering applications at stake !

Corresponding Medium article

[Acknowledgement: the code is inspired from this code]

My goal: Provide you with the keys to fully understand, explain, and implement a state-of-the-art RL method: Proximal Policy Optimization (PPO);

My tools: Python, PyTorch and Mathematical Theory;

Your takeaway: Enhanced understanding of RL techniques and recognised skills in AI, applied to PPO;

Bonus: A bioengineering application;

Key Files

src/: Contains all the application code.
checkpoint/: For storing the pretrained models' weights.
results/: For storing the figures and videos.
ppo.ipynb: Run the PPO algoritmh in a step-by-step customization.
ppo_noised.ipynb: Run the PPO algoritmh in a step-by-step customization.
ppo_25runs.ipynb: Run the PPO algoritmh 25 times to average the plots.