A repo to study basic Policy Gradient algorithms (like REINFORCE) on classic control gym environments
A policy gradient class where the algorithms are explained:
https://www.youtube.com/watch?v=_RQYWSvMyyc
A video about phenomena studied with code from this repo:
https://www.youtube.com/watch?v=gLVodUwzHyU
A further video about the code itself: https://www.youtube.com/watch?v=ib8q9ReedbM
Use python 3.
pip3 install -r requirements.txt
Actually, the main install above does it, but if you want to do everything manually...
pip3 install gym
More information here:
https://gym.openai.com/docs/#installation
pip3 install -e my_gym
And that should be it!
python3 main_pg --env_name Pendulum-v0 --nb_repet 1 --nb_cycles 500 --max_episode_steps 200 --policy_type squashedGaussian
The list of possible arguments is found in arguments.py, together with the default values