Table of Contents
Lab11 - Game Theory in Robotics
| Motivations and Goals |
|---|
| Become familiar with pursuit-evasion scenario |
| Be able to establish a greedy policy for pursuit-evasion problem |
| Tasks (teacher) |
| Implement and verify the functionality of greedy policy for pursuit-evasion problem (2 points) |
| Lab resources |
| Lab sripts: lab11 resource files |
Pursuit Evasion
A problem in computer science where a set of pursuers is trying to catch a set of evaders.
Greedy Policy
In greedy policy the next-best state is selected in each discrete step of the game simulation without considering longer prediction horizon.
Usual policies incorporate distances between individual agents as follows:
- For evaders select the most distant node to all pursuers
- For pursuers select the closest node to the closest evader
Tasks (2 points) - Lab11G
- Implement a greedy policy according to the above-described rules
- Note, you can improve the performance of the algorithm by employing more efficient distance function calculation method (e.g. Floyd-Warshall)
- Submit archived
player.pyandplanner.pyclasses into the BRUTE upload system