I created a simple module to do a Q Learning.

GitHub: Q Learning

Q Learning is one of reinforcement learning in machine learning field.

Q-Learning (Wikipedia)

#### Description

This program aims at solving a shortest path problem (maze).

Maze is defined as two dimensional array according to the following table.

Meaning Number Remark
Wall 0 Agent can’t pass through this. Walls have to cover the outer of maze.
Path 1 -
Start 2 One start is required.
Goal 3 Like start, one goal is also required.

For example, the following maze is acceptable.

const maze = [
[ 0, 2, 0, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 1, 1, 1, 1, 0, 1, 1, 1, 0 ],
[ 0, 1, 0, 0, 1, 1, 1, 0, 1, 0 ],
[ 0, 1, 0, 1, 1, 0, 0, 1, 1, 0 ],
[ 0, 1, 0, 1, 0, 0, 1, 0, 1, 0 ],
[ 0, 1, 1, 1, 1, 0, 1, 1, 1, 0 ],
[ 0, 1, 0, 1, 1, 1, 1, 0, 1, 3 ],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
];

#### Usage

Usage is as below.

1. Create a QLearning instance, and give an array of maze to it.
2. Solve the maze by learn().
3. Display the learning result by print(). Shortest way is displayed as ‘4’.

Please check example.js for entire code.

const ALPHA = 0.1;
const GAMMA = 0.9;
const EPSILON = 0.3;
const MAX = 1000;
const REWARD = 1000;
const maze = [
[ 0, 2, 0, 0, 0, 0, 0, 0, 0, 0 ],
[ 0, 1, 1, 1, 1, 0, 1, 1, 1, 0 ],
[ 0, 1, 0, 0, 1, 1, 1, 0, 1, 0 ],
[ 0, 1, 0, 1, 1, 0, 0, 1, 1, 0 ],
[ 0, 1, 0, 1, 0, 0, 1, 0, 1, 0 ],
[ 0, 1, 1, 1, 1, 0, 1, 1, 1, 0 ],
[ 0, 1, 0, 1, 1, 1, 1, 0, 1, 3 ],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
];
const ql = new QLearning(maze, ALPHA, GAMMA, EPSILON, MAX, REWARD);
ql.learn();
ql.print();
// e.g.
// 0 4 0 0 0 0 0 0 0 0
// 0 4 1 1 1 0 1 1 1 0
// 0 4 0 0 1 1 1 0 1 0
// 0 4 0 1 1 0 0 1 1 0
// 0 4 0 1 0 0 1 0 1 0
// 0 4 4 4 1 0 4 4 4 0
// 0 1 0 4 4 4 4 0 4 3
// 0 0 0 0 0 0 0 0 0 0