This repository contains a PyTorch implementation of Monte Carlo policy gradient reinforcement (REINFORCE) for discrete action spaces.
🚧 🛠️👷♀️ 🛑 Under construction...
Install the required dependencies using the following command:
pip install -r requirements.txt
You can run the algorithm on any supported Gymnasium environment. For example:
python main.py --env 'LunarLander-v2'
CartPole-v1 ![]() |
MountainCar-v0 ![]() |
Acrobot-v1 ![]() |
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LunarLander-v2 ![]() |
Asteroids-v5 ![]() |
Breakout-v5 ![]() |
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BeamRider-v5 ![]() |
Centipede-v5 ![]() |
DonkeyKong-v5 ![]() |
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Frogger-v5 ![]() |
KungFuMaster-v5 ![]() |
MarioBros-v5 ![]() |
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SpaceInvaders-v5 ![]() |
Tetris-v5 ![]() |
Gopher-v5 ![]() |
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MsPacman-v5 ![]() |
Pong-v5 ![]() |
Seaquest-v5 ![]() |
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Special thanks to Phil Tabor, an excellent teacher! I highly recommend his Youtube channel.