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RL_Pacman
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Reinforcement Learning in Pacman
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RL_Pacman
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Reinforcement Learning in Pacman ================================ Introduction ------------ In this project experimented with various MDP and Reinforcement Learning techniques namely value iteration, Q-learning and approximate Q-learning. This is part of Pacman projects developed at [UC Berkeley](http://ai.berkeley.edu/reinforcement.html). Directory Structure ------------------- ---RL [qlearningAgents.py](RL/qlearningAgents.py) [analysis.py](RL/analysis.py) ---[lab.pdf](lab.pdf) ---README.md ---[report.pdf](report.pdf) Executing --------- Then run the autograder using $python autograder.py It gave me a score of 25/25. Value Iteration --------------- $python gridworld.py -a value -i 100 -k 10 $python gridworld.py -a value -i 5 Bridge Crossing Analysis ------------------------ $python gridworld.py -a value -i 100 -g BridgeGrid --discount 0.9 --noise 0.2 Policies -------- $python autograder.py -q q3 Q-Learning ---------- python gridworld.py -a q -k 5 -m Epsilon Greedy -------------- $python gridworld.py -a q -k 100 $python crawler.py Bridge Crossing Revisited ------------------------- $python gridworld.py -a q -k 50 -n 0 -g BridgeGrid -e 1 Q-Learning and Pacman --------------------- $python pacman.py -p PacmanQAgent -x 2000 -n 2010 -l smallGrid Approximate Q-Learning ---------------------- $python pacman.py -p ApproximateQAgent -x 2000 -n 2010 -l smallGrid $python pacman.py -p ApproximateQAgent -a extractor=SimpleExtractor -x 50 -n 60 -l mediumGrid $python pacman.py -p ApproximateQAgent -a extractor=SimpleExtractor -x 50 -n 60 -l mediumClassic Developed by ------------ [Sai Srinadhu K](https://www.linkedin.com/in/sai-srinadhu-katta-a189ab11b/)
