PongReinforcementLearning

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PongReinforcementLearning

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Deep DQN Based Reinforcement Learning for simple Pong PyGame

PongReinforcementLearning 최신버젼 다운로드

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## Pong Game Reinforcement Learning ##
Deep DQN Based Reinforcement Learning for simple Pong PyGame.  This python based RL experiment plays a Py Pong Game (DQN control of Left Hand Yellow Paddle against a programmed RHS Paddle)

![alt text](https://github.com/JulesVerny/PongReinforcementLearning/blob/master/PongPicture2.PNG "Game Play")

The Objective is simply measured as successfully returning of the Ball by the Yellow RL DQN Agent.  
The programmed opponent player is a pretty hot player. So success as is simply the  ability to return ball served from Serena Williams.
The Moving Average Score is calculated in range from [-10, +10] from Complete failure to return the balls, to full success in returning the Ball. This experiment demonstrates DQN based Reinforcement Learning Agent, which improves from poor performace ~ -9.0 towards reasonably good ~ +9.0 (Fluctuating) return rate in around 10,000 game update cycles. The Agent employs Direct game Features [ Paddle Y, Ball X, Y and Ball X, Y Directions feeding into DQN Nueral net Estimator of the Q[S,A] function.

This is NOT a Convolutional Network based RL [based Game Video Frame states, because in my experience CN's takes much too Long to Learn on standard PCs: GPUs] and so unfortunately this is Game Specific DQN Reinforecment Learning, and cannot be generalised to other Games like Deep minds ATARI game approach based upon Convolutional Network layers.

Please see my other repository for a Convolotional DQN game of Pong
https://github.com/JulesVerny/PongConvolutionalDQN

![alt text](https://github.com/JulesVerny/PongReinforcementLearning/blob/master/ScoreGrowth.png "Score growth")

### Useage ##
python MyExperiment.py
### Main Python Package Dependencies ###
pygame, keras [hence TensorFlow,Theano], numpy, matplotlib
### Acknowledgments: ###
* The  Pong Game Code is based upon Siraj Raval's inspiring vidoes on Machine learning and Reinforcement Learning [ Which does employ full convolutional DQN example]:   https://github.com/llSourcell/pong_neural_network_live

* The DQN Agent Software is Based upon Jaromir Janisch source code: 
https://jaromiru.com/2016/10/03/lets-make-a-dqn-implementation/

* Daniel Slaters Blog & Examples:
http://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html?showComment=1502902115538

* WILDML Reinforcement Learning Summary (Examples):
http://www.wildml.com/2016/10/learning-reinforcement-learning/

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