Pacman and Ghost Agent | Python | Artificial Intelligence | Search-based Algorithms | Learning-based Algorithms
Project 1 In this project, the Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. General search algorithms are bubilt and applied to multiple Pacman scenarios.
Project 2 In this project, agents for the classic version of Pacman including ghosts are designed. Along the way, implementation of both minimax and expectimax search is done along with evaluation function design.
Project 3 In this project, value iteration and Q-learning has been implemetned. The agents are first tested on Gridworld (from class), and then run in a simulated robot controller (Crawler) and Pacman.
Project 4 This project deals with designing Pac-Man agents that use sensors to locate and eat invisible ghosts. The code moves from locating single, stationary ghosts to hunting packs of multiple moving ghosts with ruthless efficiency.
Contest The contest involves a multi-player capture-the-flag variant of Pacman, where agents control both Pacman and ghosts in coordinated team-based strategies. My team(of two agents) try to eat the food on the far side of the map, while defending the food on your home side.