hmomin/PPO-Winter-Run
Trains an agent with Proximal Policy Optimization (PPO) to beat Winter Run
This project helps anyone interested in training an AI agent to master the browser-based game 'Winter Run'. You provide the game's environment, and the system trains an agent using reinforcement learning to play optimally. It’s for enthusiasts, students, or researchers exploring deep reinforcement learning applied to simple web games.
No commits in the last 6 months.
Use this if you want to see how a deep reinforcement learning agent, specifically using Proximal Policy Optimization (PPO), can learn to play and beat a simple browser game directly within your web browser.
Not ideal if you are looking to apply reinforcement learning to complex, high-fidelity simulations or real-world robotics, as this project focuses on a specific browser game.
Stars
23
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Language
TypeScript
License
MIT
Category
Last pushed
May 21, 2022
Commits (30d)
0
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