The Pac-Man projects teach foundational AI concepts. The projects allow you to visualize the results of the techniques you implement. Pac-Man provides a challenging problem environment that demands creative solutions.
In this project, Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. I have built general search algorithms that apply to Pacman scenarios.
The files i edited are search.py and searchAgents.py.
Vizualize Algorithms
Q1: Finding a Fixed Food Dot using Depth First Search
python pacman.py -l mediumMaze -p SearchAgent
Q2: Breadth First Search
python pacman.py -l mediumMaze -p SearchAgent -a fn=bfs
Q3: A* search
python pacman.py -l bigMaze -z .5 -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic
Q4: Finding all the corners
python pacman.py -l tinyCorners -p SearchAgent -a fn=bfs,prob=CornersProblem
Q5: Corners Problem with Heuristic
python pacman.py -l mediumCorners -p AStarCornersAgent -z 0.5
Q6: Eating All The Dots (!need to wait ~1 min)
python pacman.py -l trickySearch -p AStarFoodSearchAgent
Q7: Suboptimal Search(greedily eats the closest dot)
python pacman.py -l bigSearch -p ClosestDotSearchAgent -z .5
In this project, i have designed agents for the classic version of Pacman, including ghosts. Along the way, i implemented both minimax, expectimax search and evaluation functions.
The files i edited are multiAgents.py and pacman.py.
Vizualize Algorithms
Q1: Improve the ReflexAgent to play respectably
python pacman.py --frameTime 0 -p ReflexAgent -k 2
Q2: Minimax search
python autograder.py -q q2 --no-graphics
Q3: Alpha-Beta Pruning
python autograder.py -q q3
Q4: Expectimax(probabilistic behavior of agents who may make suboptimal choices)
python autograder.py -q q4
Q5: Evaluation Function
python autograder.py -q q5