This page is located in archive. Go to the latest version of this course pages.

Task13 - Value-iteration policy in pursuit-evasion

The main task is to implement Value-iteration policy for robotics pursuit-evasion game.

Deadline 13. January 2019, 23:59 PST
Points 6
Label in BRUTE Task13
Files to submit archive with player
Minimal content of the archive: player/Player.py
Do not submit the .policy files with the stored precalculate policy!
Resources Task11 resource files


In file player/Player.py in function value_iteration_policy implement the Value-iteration policy decision making for pursuit-evasion game.

The Value-iteration policy is an asymptotically optimal decision making approach. The next-best state is selected in each discrete step of the game based on its value.

The value_iteration_policy function has the following prescription which follows the prescription of the greedy_policy from Task11 - Greedy policy in pursuit-evasion:

           def value_iteration_policy(self, gridmap, evaders, pursuers):
        Method to calculate the value-iteration policy action
        gridmap: GridMap
            Map of the environment
        evaders: list((int,int))
            list of coordinates of evaders in the game (except the player's robots, if he is evader)
        pursuers: list((int,int))
            list of coordinates of pursuers in the game (except the player's robots, if he is pursuer)

The purpose of the function is to internally update the self.next_robots variable, which is a list of (int, int) robot coordinates based on the current state of the game, given gridmap grid map of the environment and the player's role self.role. The player is given the list evaders of all evading robots in the game other than his robots and the list of pursuers of all pursuing robots in the game other than his robots. I.e., the complete set of robots in game is given as the union of evaders, pursuers and self.robots.

During the gameplay, each player is asked to update their intention for the next move coded in the self.next_robots variable by calling the calculate_step function. Afterward, the step is performed by calling the take_step̈́ function followed by the game checking each step, whether it complies to the rules of the game.

The game ends after a predefined number of steps or when all the evaders are captured.

The number of players and robots is fixed for this task. There will be two players in the game. One player with a single evading robot and one player with two pursuers.

In value-iteration the strategies for different configurations may be stored in the self.values variable which is either calculated from scratch, or loaded from file, if the policy already exists. The provided code for loading the value-iteration policy may be modified; however, the code shall use pickle library for saving and loading the data to and from the .policy files.


The code can be evaluated using the following set of game scenarios.
Additional Game Scenarios

The evaluation code extends for:

    games = [("grid", "games/grid_6.game"),
             ("grid", "games/grid_7.game"),
             ("grid", "games/grid_8.game"),
             ("pacman_small", "games/pacman_small_5.game"),
             ("pacman_small", "games/pacman_small_6.game")]

Note, you can easily generate new game setups by modifying the .game files accordingly. In the upload system, the student's solutions are tested against the teachers RANDOM and GREEDY policies players. Note, the calculation of the VALUE_ITERATION policy is computationally expensive, therefore is the time for running the evaluation limited to 10 minutes.

courses/b4m36uir/hw/task13.txt · Last modified: 2019/01/07 13:42 by cizekpe6