Playing Atari with Reinforcement Learning

Entity Detection for Space Invaders
Entity Detection for Space Invaders

This project explored the viability of using reinforcement learning to play Atari games using only pixel data as input. Whereas Google’s DeepMind used a convolutional neural network as the front end for feature extraction, we experimented with less complex feature extraction methods. I implemented edge-based feature extraction and a DBScan clustering algorithm that dynamically labeled extracted features. These features were then used as the input to a Q-Learning algorithm, which learned optimal actions for multiple atari games. What we learned: for simple games, you can learn to do okay; as the games get more complex deep learning methods simply outperform traditional methods.

Developed For

This project was developed for CS221 and CS229 at Stanford University

Launch Project

Project Paper (pdf)

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