![]() Note: The evaluation function you’re writing is evaluating state-action pairs in later parts of the project, you’ll be evaluating states. ![]() Note: As features, try the reciprocal of important values (such as distance to food) rather than just the values themselves. Note: Remember that newFood has the function asList() How does your agent fare? It will likely often die with 2 ghosts on the default board, unless your evaluation function is quite good. Python pacman.py -frameTime 0 -p ReflexAgent -k 2 But, we don’t know when or how to help unless you ask.ĭiscussion: Please be careful not to post spoilers.įirst, play a game of classic Pacman by running the following command: We want these projects to be rewarding and instructional, not frustrating and demoralizing. If you can’t make our office hours, let us know and we will schedule more. Office hours, section, and the discussion forum are there for your support please use them. Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. If you do, we will pursue the strongest consequences available to us. We trust you all to submit your own work only please don’t let us down. These cheat detectors are quite hard to fool, so please don’t try. ![]() If you copy someone else’s code and submit it with minor changes, we will know. If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work.Īcademic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. However, the correctness of your implementation – not the autograder’s judgements – will be the final judge of your score. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. Please do not change the other files in this distribution or submit any of our original files other than this file.Įvaluation: Your code will be autograded for technical correctness. Once you have completed the assignment, you will submit a token generated by submission_autograder.py. Project 2 specific autograding test classesįiles to Edit and Submit: You will fill in portions of multiAgents.py during the assignment. Parses autograder test and solution filesĭirectory containing the test cases for each question You don't need to use these for this project, but may find other functions defined here to be useful.Ĭode for reading layout files and storing their contents Useful data structures for implementing search algorithms. This file describes several supporting types like AgentState, Agent, Direction, and Grid. The logic behind how the Pacman world works. This file also describes a Pacman GameState type, which you will use extensively in this project. Spring: 3.0 hours of web-based lecture and 1.Where all of your multi-agent search agents will reside. A deficient grade in COMPSCI W182 may be removed by taking COMPSCI 182, or COMPSCI L182.įall: 3.0 hours of web-based lecture and 1.0 hours of discussion per week Prerequisites: MATH 53 and MATH 54 or equivalent COMPSCI 70 or STAT 134 or EECS 126 COMPSCI 61B or equivalent and COMPSCI 189 (recommended).Ĭredit Restrictions: Students will receive no credit for COMPSCI W182 after completing COMPSCI 182, or COMPSCI L182. Exploring the training and use of deep networks with visualization tools. Methods with formal guarantees: generative and adversarial models, tensor factorization., Students will come to understand visualizing deep networks. Student Learning Outcomes: Students will learn design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization., Understanding deep networks. Practical implementations, empirical studies, and scientific analyses." This course attempts to cover that ground. In Yann Lecun's words they require "an interplay between intuitive insights, theoretical modeling, They do not however, follow a closed or compact set of theoretical principles. They have growing impact in many other areas of science and engineering. Catalog Description: Deep Networks have revolutionized computer vision, language technology, robotics and control.
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