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__pycache__
__pycache__
 
 
layouts
layouts
 
 
test_cases
test_cases
 
 
README.md
README.md
 
 
VERSION
VERSION
 
 
analysis.py
analysis.py
 
 
autograder.py
autograder.py
 
 
backend.py
backend.py
 
 
crawler.py
crawler.py
 
 
deepQLearningAgents.py
deepQLearningAgents.py
 
 
environment.py
environment.py
 
 
featureExtractors.py
featureExtractors.py
 
 
game.py
game.py
 
 
ghostAgents.py
ghostAgents.py
 
 
grading.py
grading.py
 
 
graphicsCrawlerDisplay.py
graphicsCrawlerDisplay.py
 
 
graphicsDisplay.py
graphicsDisplay.py
 
 
graphicsGridworldDisplay.py
graphicsGridworldDisplay.py
 
 
graphicsUtils.py
graphicsUtils.py
 
 
gridworld.py
gridworld.py
 
 
keyboardAgents.py
keyboardAgents.py
 
 
layout.py
layout.py
 
 
learningAgents.py
learningAgents.py
 
 
mdp.py
mdp.py
 
 
model.py
model.py
 
 
nn.py
nn.py
 
 
pacman.py
pacman.py
 
 
pacmanAgents.py
pacmanAgents.py
 
 
projectParams.py
projectParams.py
 
 
qlearningAgents.py
qlearningAgents.py
 
 
reinforcementTestClasses.py
reinforcementTestClasses.py
 
 
testClasses.py
testClasses.py
 
 
testParser.py
testParser.py
 
 
textDisplay.py
textDisplay.py
 
 
textGridworldDisplay.py
textGridworldDisplay.py
 
 
util.py
util.py
 
 
valueIterationAgents.py
valueIterationAgents.py
 
 

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Project 4: Reinforcement Learning for Pacman

Used Value Iteration, Q-learning, and Approximate Q-learning reinforcement techniques to allow a Pacman agent to navigate the map. Adapted feature vectors to store state information and train the agent.

https://inst.eecs.berkeley.edu/~cs188/sp23/projects/proj4/

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