Skip to content

Samu has learnt the rules of Conway's Game of Life

License

Notifications You must be signed in to change notification settings

nbatfai/SamuLife

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

34 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

SamuLife

Samu (Nahshon) has learnt the rules of Conway's Game of Life. This is an example of the paper entitled "Samu in his prenatal development".

This project uses the same COP-based Q-learning engine as Samu (Nahshon) to predict the next sentences of a conversation.

Experiments

  • #1: https://youtu.be/p936TRxfC2I It works very well, but its algorithm is unreasonable (the interpretation of the rewarding is unreasonable).
  • #2, v0.0.1-buggyQL: https://youtu.be/WNrf9OhSbPQ The behaviour and the algorithm of this is the same as the previous one.
  • #3, v0.0.2-manySamus-withoutNN-toYouTube: (so many little Samus, without neural network architecture, it uses a simple lookup table) https://youtu.be/BnNVM8XNDSA This algorithm has already been reasonable and it has worked very well.
  • #4, v0.0.3-manySamus-withNN-toYouTube: https://youtu.be/b60m__3I-UM If this algorithm is the same as the previous (reasonable but now with neural networks to approximate the Q values) then it has not yet worked very well. But with a modified perception, this algorithm has also been reasonable and it has worked very well too.
  • #5, v0.0.4-predicting-the-present-eliminated: It is important to note that in the previous version the COP-based Q-learning has become trivial because after a short starting period it chooses the Q-action that was passed in as the actual cell state argument. It means that the agent does not predict the future but the present. This version has already been improved. It really predicts the future that can be seen well in the video at https://youtu.be/j6bus5efESU
  • #6, v0.0.5-buggy+rewardtable:

Usage

git clone https://github.com/nbatfai/SamuLife.git
cd SamuLife/
~/Qt/5.5/gcc_64/bin/qmake SamuLife.pro
make
./SamuLife

Experiments with this project

v0.0.1-buggyQL

git clone https://github.com/nbatfai/SamuLife.git
cd SamuLife/
git checkout v0.0.1-buggyQL
~/Qt/5.5/gcc_64/bin/qmake SamuLife.pro
make
./SamuLife

https://youtu.be/WNrf9OhSbPQ

samulife

v0.0.2-manySamus-withoutNN-toYouTube

git clone https://github.com/nbatfai/SamuLife.git
cd SamuLife/
git checkout v0.0.2-manySamus-withoutNN-toYouTube
~/Qt/5.5/gcc_64/bin/qmake SamuLife.pro
make
./SamuLife

https://youtu.be/BnNVM8XNDSA

slexp3

v0.0.3-manySamus-withNN-toYouTube

git clone https://github.com/nbatfai/SamuLife.git
cd SamuLife/
git checkout v0.0.3-manySamus-withNN-toYouTube
~/Qt/5.5/gcc_64/bin/qmake SamuLife.pro
make
./SamuLife

https://youtu.be/b60m__3I-UM

slexp4

v0.0.4-predicting-the-present-eliminated

It is important to note that in the previous version the COP-based Q-learning has become trivial because after a short starting period it chooses the Q-action that was passed in as the actual cell state argument. It means that the agent does not predict the future but the present. This version has already been improved. It really predicts the future that can be seen well in the video at https://youtu.be/j6bus5efESU

git clone https://github.com/nbatfai/SamuLife.git
cd SamuLife/
git checkout v0.0.4-predicting-the-present-eliminated
~/Qt/5.5/gcc_64/bin/qmake SamuLife.pro
make
./SamuLife

https://youtu.be/j6bus5efESU

screenshot from 2016-02-04 11-10-17

Other experiments

Samu (Nahshon) http://arxiv.org/abs/1511.02889, https://github.com/nbatfai/nahshon


SamuMovie https://github.com/nbatfai/SamuMovie, https://youtu.be/XOPORbI1hz4

SamuStroop https://github.com/nbatfai/SamuStroop, https://youtu.be/6elIla_bIrw, https://youtu.be/VujHHeYuzIk

SamuBrain https://github.com/nbatfai/SamuBrain

SamuCopy https://github.com/nbatfai/SamuCopy


SamuTicker https://github.com/nbatfai/SamuTicker

SamuVocab https://github.com/nbatfai/SamuVocab

About

Samu has learnt the rules of Conway's Game of Life

Resources

License

Stars

Watchers

Forks

Packages

No packages published