This repo is deprecated - please visit our new repo https://github.com/vwxyzjn/ppo-implementation-details and the improved ICLR 2022 blog post on PPO https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/

This repo contains the source code for the PPO Implementation Deep Dive tutorial series.

1. Proximal Policy Optimization Implementation Deep Dive | 11 Core Implementation Details (youtu.be/MEt6rrxH8W4)
2. Proximal Policy Optimization Implementation Deep Dive | 9 Atari-specific Details (youtu.be/05RMTj-2K_Y)
3. Proximal Policy Optimization Implementation Deep Dive | 8 Details for Continuous Actions (youtu.be/BvZvx7ENZBw)

You can find out where theses implementation details come from by visiting my blog post, which contains github permanent links of the details to the original implementation.

If you like this repo, consider also checking out CleanRL, my RL library based on single-file implementations.

Prerequisites:

• Python 3.8+
• Poetry

Install dependencies:

poetry install

Train agents:

poetry run python ppo.py

Train agents with experiment tracking:

poetry run python ppo.py --track --capture-video

Install dependencies:

poetry install -E atari

Train agents:

poetry run python ppo_atari.py

Train agents with experiment tracking:

poetry run python ppo_atari.py --track --capture-video

Install dependencies:

poetry install -E pybullet

Train agents:

poetry run python ppo_continuous_action.py

Train agents with experiment tracking:

poetry run python ppo_continuous_action.py --track --capture-video

!! Note this installation method only works in Linux

Install dependencies:

poetry install -E mujoco
poetry run python -c "import mujoco_py"

Train agents:

poetry run python ppo_continuous_action.py --gym-id Hopper-v2

Train agents with experiment tracking:

poetry run python ppo_continuous_action.py --gym-id Hopper-v2 --track --capture-video