I’ve used Deep Q Learning to learn control policies to play the game of pong, directly from visual data. The model is based on a Convolutional Neural Network that learns to use the input raw pixel data , to estimate a value function that allows us to approximate the future rewards, for any given action. The exact same model has been trained in different environments, and plays a near perfect game, against a perfect rival bot, as well as against a human. Experiments have also been conducted to better understand and evaluate how our trained agent responds to dynamic changes in the environment.
Acknowledgements :
- Machine Learning with Phil YTC Channel -> Great resources
- The Coding Train for PyGame Tutorials (For the simulator)
- Countless Video Tutorials on Youtube, on whom this work is based