AI-Utility-Based-Markov-Decision-Process

By using of utility approach for path finding in AI, the script prints the optimal states to follow with arrows UP, DOWN, RIGHT or LEFT.

WALL is wall (you can type any word less than 10 length instead of |), . is empty space, +1 positive reward and -1 negative reward

Map Matrix

[

        [".",   ".",    ".",    ".",    "+1"],
        
        [".",   ".",    ".",    ".",    "-1"],
        
        [".",   "|",    ".",    ".",    "."],
        
        [".",   ".",    ".",    ".",    "."]

]

getUtilityCalculatedMap()

Output is like for given map matrix

printArrowMap()

RIGHT RIGHT RIGHT RIGHT +1

RIGHT RIGHT UP UP -1

UP | UP UP DOWN

UP RIGHT UP UP DOWN

printUtilityMap()

0.3210 0.4388 0.5860 0.7700 1.0000

0.3251 0.4038 0.4874 0.4860 -1.0000

0.2301 0.0000 0.3955 0.3558 -0.0300

0.1760 0.2193 0.3116 0.2516 -0.0300

Map Trip

Map trip is just a console simulation of moving agent without learning to see the time of travel depending on full randomness