vietnh1009/Super-mario-bros-PPO-pytorch
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
This project helps reinforcement learning researchers and AI enthusiasts train an agent to master the classic video game Super Mario Bros. By applying the Proximal Policy Optimization (PPO) algorithm, you can input the game environment and output a highly capable AI agent that can complete almost all levels of the game. It is designed for those looking to explore advanced AI training for game-playing.
1,268 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or student focused on reinforcement learning, specifically interested in applying and evaluating the PPO algorithm for game AI.
Not ideal if you are looking for a plug-and-play solution for general game AI or if you are not comfortable with machine learning model training concepts.
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Language
Python
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MIT
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Last pushed
Jul 24, 2021
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