Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder.
PYTHON PRIORITY QUEUE UPDATE VALUE CODE
You do not need to submit any other files.Įvaluation: Your code will be autograded for technical correctness. It contains the evaluation results from your local autograder, and a copy of all your code. Note: You only need to submit reinforcement.token, generated by running submission_autograder.py. Please do not change the other files in this distribution or submit any of our original files other than these file. Project 3 specific autograding test classesįiles to Edit and Submit: You will fill in portions of valueIterationAgents.py, qlearningAgents.py, and analysis.py during the assignment. Parses autograder test and solution filesĭirectory containing the test cases for each question Plug-in for the Gridworld text interface. Used for the approximate Q-learning agent (in qlearningAgents.py).Ībstract class for general reinforcement learning environments. Utilities, including util.Counter, which is particularly useful for Q-learners.Ĭlasses for extracting features on (state, action) pairs.
Q-learning agents for Gridworld, Crawler and Pacman.Ī file to put your answers to questions given in the project.ĭefines the base classes ValueEstimationAgent and QLearningAgent, which your agents will extend. Files you'll edit:Ī value iteration agent for solving known MDPs. The code for this project contains the following files, available as a zip archive. Note: In this project you only need to submit the token generated by submission_autograder.py. It can be run for one particular test by commands of the form: python autograder.py -t test_cases/q2/1-bridge-grid It can be run for one particular question, such as q2, by: python autograder.py -q q2
This can be run on all questions with the command: python autograder.py You will test your agents first on Gridworld (from class), then apply them to a simulated robot controller (Crawler) and Pacman.Īs in previous projects, this project includes an autograder for you to grade your solutions on your machine. In this project, you will implement value iteration and Q-learning.
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