In this project, you will implement value iteration and Q-learning. You will test your agents first on Gridworld (from class), then apply them to a simulated robot controller (Crawler) and Pacman.

As in previous projects, this project includes an autograder for you to grade your solutions on your machine. This can be run on all questions with the command: It can be run for one particular question, such as q2, by: It can be run for one particular test by commands of the form: The code for this project contains the following files, which are available in a zip archive: Files to Edit and Submit: You will fill in portions of valueIterationAgents.py, qlearningAgents.py, and analysis.py during the assignment.

You should submit these files with your code and comments. Please do not change the other files in this distribution or submit any of our original files other than these files.

Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder.

However, the correctness of your implementation — not the autograder’s judgements — will be the final judge of your score. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work. Read more from ai.berkeley.edu…

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