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This repository has been archived by the owner on May 8, 2024. It is now read-only.

Mufas61/reinforcement-learning

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ReinforcementLearning

This program shows how an Q-learning algorithm works in a maze and has a maze-editor to create own test senarios.

Developed with the help of an already existing program: http://www.cs.cmu.edu/~awm/rlsim/

How to start the application

The application comes with a gradle wrapper, which loads all dependencies and runs the application with the command:

sh gradlew run

Q-Learning

Q(s,a) = Q(s,a) + α [r + γ * Q(s',a') - Q(s,a)]

Legend:

  • s - state
  • a - action
  • s'/a' - next state/action
  • r - reward/penalty
  • γ - discounting rate
  • α - learning rate

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