CSE 571 Artificial Intelligence
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#a-star-search#alpha-beta-pruning#artificial-intelligence#breadth-first-search#deep-reinforcement-learning#depth-first-search#epsilon-greedy#expectimax#function-approximation#greedy-search#minimax-algorithm#neural-networks#particle-filter-tracking#policy-iteration#q-learning#reinforcement-learning#uniform-cost-search#value-iteration
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Artificial-Intelligence-Pacman-Project
Arizona State UniversityCSE 571 Artificial Intelligence
Fall 2017
Grade A-
In this project, several techniques of Artificial Intelligence such as Searching, Adversarial Behaviour, Deep Reinforcement Learning, Neural Network etc are implemented to help the pacman agent to maximize its expected utility.
- Project 0: Python Autograder Tutorial
- Project 1: Searching - DFS, BFS, UCS, Greedy Search, A* Search, etc.
- Project 2: MultiAgent Pacman - minimax, alpha-beta pruning, expectimax, etc.
- Project 3: Reinforcement Learning - MDP, value iteration, q-learning, epsilon-greedy, approximate q-learning, etc.
- Project 4: Ghostbusters - HMM, Particle Filtering, Bayes' Nets, Deep Reinforcement Learning, etc.
Programming Language - Python 2.7
