Learned about search problems (A*, CSP, minimax), reinforcement learning, bayes nets, hidden markov models, and machine learning. Completed all homeworks, projects, midterms, and finals in 5 weeks.
Implemented Depth First Search, Breadth First Search, Uniform Cost Search, and A* Search. Created different heuristics. Helped pacman agent find shortest path to eat all dots.
Created basic reflex agent based on a variety of parameters. Improved agent to use minimax algorithm (with alpha-beta pruning). Implemented expectimax for random ghost agents. Improved evaluation function for pacman states.
Worked with Markov Decision Processes. Started with value iteration agent. Then, worked on changing noise and discount parameters to enact different policies. Built Q-Learning agent and an Epsilon Greedy agent. Then, used reinforcement learning to approximate Q-Values.
Updated belief distribuition of ghost agents based on sequential noise readings and distribution of future ghost agent states. Moved pacman based on closest likely ghost location. Then, used particle filtering to achieve the same result. Finally, implemented joint particle filtering algorithm with multiple reactive ghosts.
Implemented perceptron classifier and MIRA classifier to read handwritten digits. Improved features used to distinguish digits. Applied machine learning to pacman games. Improved pacman features.
