Navigating in unknown real world is a key challenge in autonomous vehicle or mobile robot application. In this project, the problem is simplified as a robot navigating in an unknown maze and finding its optimal path.
The scope of the project is to develop a motion planning algorithm that enables a robot to explore an unknown maze (environment), to learn the maze layout, and then to find its fastest path from a corner of the maze to its center.
Used Reinforcement Learning to learn an unknown maze and to find its the fastest path.
To run the program:
python tester.py test_maze_01.txt
To display the maze:
python showmaze.py test_maze_01.txt
- "Multiple steps at one move" has not been implemented.
- The number of training steps is set by
training_end = 40000
in robot.py. It must be less than 99000. Larger it is (=more traing), the algorithm produces more consistent an optimal result. - Parameters such as alpha, gamma and epilson have not been optimized.