HackOn2 | Pac-man | Mohtasham
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In this project , we used value iteration for the agent that will choose action that will maximize
expected utility.With value iteration , always calculate state’s true value until it converges.The
state’s value is the optimal policy. Q learning agent does not need to have prior information about
model of the environment as it learn by process of trial and error.We also implemented
approximate q learning agent by using weighted feature of the states. By pacman game, we found
that approximate q learning agent has higher rate of winning game than normal q learning agent.
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