Releases: xujiachang1024/MDP-Pac-Man
MDP Pac-Man v1.0
Design and Implementation of Pac-Man Strategies with Embedded Markov Decision Process in a Dynamic, Non-Deterministic, Fully Observable Environment
Abstract
This project designs and implements Pac-Man strategies, whose decision-making protocol is solely based on Markov Decision Process, without the support of pathfinding algorithms nor heuristic functions, in a dynamic, non-deterministic, fully observable environment. This project provides the rare opportunity to refine the understanding of, and practice the application of Markov Decision Process in a classic arcade game setting. After significant number of hours of parameter tuning, my design achieved a win rate ranging between 50% and 60%. With the proven effectiveness of embedded Markov Decision Process, a spin-off Pac-Man AI project that incorporates the advantages of pathfinding algorithms, heuristic functions, and Markov Decision Process shall be on the agenda.
Design Document
Style of Work
- Coding style complies with industry standard
- Design paper complies with IEEE Conference format
How to run
This version of Intelligent Pac-Man is running on Python 2.7 environment. The visibility of the agent in this Pac-Man project is extremely limited, just like someone is running through the maze for real.
Run the following command to play the game:
python pacman.py --pacman MDPAgent --layout mediumClassic --numGames 50
MDP Pac-Man v0.0.1
Design and Implementation of Pac-Man Strategies with Embedded Markov Decision Process in a Dynamic, Non-Deterministic, Fully Observable Environment
Abstract
This project designs and implements Pac-Man strategies, whose decision-making protocol is solely based on Markov Decision Process, without the support of pathfinding algorithms nor heuristic functions, in a dynamic, non-deterministic, fully observable environment. This project provides the rare opportunity to refine the understanding of, and practice the application of Markov Decision Process in a classic arcade game setting. After significant number of hours of parameter tuning, my design achieved a win rate ranging between 50% and 60%. With the proven effectiveness of embedded Markov Decision Process, a spin-off Pac-Man AI project that incorporates the advantages of pathfinding algorithms, heuristic functions, and Markov Decision Process shall be on the agenda.
Design Document
Style of Work
- Coding style complies with industry standard
- Design paper complies with IEEE Conference format
How to run
This version of Intelligent Pac-Man is running on Python 2.7 environment. The visibility of the agent in this Pac-Man project is extremely limited, just like someone is running through the maze for real.
Run the following command to play the game:
python pacman.py --pacman MDPAgent --layout mediumClassic --numGames 50
Warning
This MDP Pac-Man only iterates through free spaces.