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Particle Filters and Ghost TrackingBayesian inference for tracking invisible ghosts in Pacman. Exact inference with the forward algorithm, particle filters, and joint tracking of multiple ghosts.
8 min -
Value Iteration and Q-LearningMDPs, Bellman equations, batch value iteration, tabular Q-learning with epsilon-greedy exploration, and approximate Q-learning with feature weights.
9 min -
Minimax, Alpha-Beta, and ExpectimaxMulti-agent search in Pacman. Minimax with depth-limited game trees, alpha-beta pruning, expectimax for random ghosts, and evaluation functions.
8 min -
DFS, BFS, UCS, and A* in PacmanImplementing graph search algorithms in the Berkeley Pacman framework. DFS, BFS, UCS, A*, plus heuristics for corners and food.
8 min
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