![]() Python pacman.py -p MinimaxAgent -l minimaxClassic -a depth=4 Alpha-Beta Pruning This will show what your algorithm does on a number of small trees, as well as a pacman game. ![]() If you call it any more or less than necessary, the autograder will complain. As a result, the autograder will be very picky about how many times you call GameState.generateSuccessor. This is the only way reliable way to detect some very subtle bugs in implementations of minimax. Grading: We will be checking your code to determine whether it explores the correct number of game states. ![]() Important: A single search ply is considered to be one Pacman move and all the ghosts' responses, so depth 2 search will involve Pacman and each ghost moving two times. MinimaxAgent extends MultiAgentSearchAgent, which gives access to pth and self.evaluationFunction. Score the leaves of your minimax tree with the supplied self.evaluationFunction. The code should also expand the game tree to an arbitrary depth. In particular, the minimax tree has multiple min layers (one for each ghost) for every max layer. Minimax agent works with any number of ghosts. Wrote an adversarial search agent in the provided MinimaxAgent class stub in multiAgents.py. Python autograder.py -q q1 -no-graphics Minimax Turn off graphics with -q to run lots of games quickly. You can also play multiple games in a row with -n. If the randomness is preventing you from telling whether your agent is improving, you can use -f to run with a fixed random seed (same random choices every game). Python pacman.py -frameTime 0 -p ReflexAgent -k 2ĭefault ghosts are random you can also play for fun with slightly smarter directional ghosts using -g DirectionalGhost. Python pacman.py -frameTime 0 -p ReflexAgent -k 1 Python pacman.py -p ReflexAgent -l testClassic Implemented reflex agent to consider both food locations and ghosts locations to perform well Design Agents for the classic version of Pac-Man including ghosts.
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