Multiagent pacman github

In this project, you will design agents for the classic version of Pacman, including ghosts. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design.

The code base has not changed much from the previous project, but please start with a fresh installation, rather than intermingling files from project 1. As in project 1, this project includes an autograder for you to grade your answers on your machine.

This can be run on all questions with the command:. Note: If your python refers to Python 2. By default, the autograder displays graphics with the -t option, but doesn't with the -q option. You can force graphics by using the --graphics flag, or force no graphics by using the --no-graphics flag.

The code for this project contains the following files, available as a zip archive. Files to Edit and Submit: You will fill in portions of multiAgents.

You should submit this file with your code and comments. Please do not change the other files in this distribution or submit any of our original files other than this file. 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. Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes, we will know.

These cheat detectors are quite hard to fool, so please don't try. We trust you all to submit your own work only; please don't let us down. If you do, we will pursue the strongest consequences available to us. Getting Help: You are not alone! If you find yourself stuck on something, talk with other classmates first, and contact the course staff for help.

Office hours and the discussion forum are there for your support; please use them. We want these projects to be rewarding and instructional, not frustrating and demoralizing. Now, run the provided ReflexAgent in multiAgents. Improve the ReflexAgent in multiAgents. The provided reflex agent code provides some helpful examples of methods that query the GameState for information.

A capable reflex agent will have to consider both food locations and ghost locations to perform well. Your agent should easily and reliably clear the testClassic layout:. Try out your reflex agent on the default mediumClassic layout with one ghost or two and animation off to speed up the display :.

How does your agent fare? It will likely often die with 2 ghosts on the default board, unless your evaluation function is quite good. Note: As features, try the reciprocal of important values such as distance to food rather than just the values themselves.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.

If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

multiAgents.py

In this problem, you will design agents for the classic version of Pac-Man, including ghosts. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design.

This file also describes a Pac-Man GameState type, which you will use extensively in this project game. The AlphaBetaAgent minimax values should be identical to the MinimaxAgent minimax values, although the actions it selects can vary because of different tie-breaking behavior.

Mini-Contest 1: Multi-Agent Pacman

Again, the minimax values of the initial state in the minimaxClassic layout are 9, 8, 7 and for depths 1, 2, 3 and 4 respectively. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. No description, website, or topics provided. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit Fetching latest commit…. The code for this project contains the following files, available as multiagent. Question 1 : Improve the ReflexAgent in multiAgents. The provided reflex agent code provides some helpful examples of methods that query the GameState for information.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. You are free to use and extend these projects for educational purposes. The code below is provided as a guide. You are welcome to change it in any way you see fit, so long as you don't touch our method headers.

Just like in the previous project, getAction takes a gameState and returns some Directions. The evaluation function takes in the current and proposed successor gameStates pacman. The code below extracts some useful information from the state, like the remaining food newFood and Pacman position after moving newPos. Print out these variables to see what you're getting, then combine them to create a masterful evaluation function.

The score is the same one displayed in the Pacman GUI. This evaluation function is meant for use with adversarial search agents not reflex agents. Please do not remove anything, however.

Note: this is an abstract class: one that should not be instantiated. It's only partially specified, and designed to be extended. Agent game. Here are some method calls that might be useful when implementing minimax. Without any disturbance of ghost, the pacman would go after the closest food, similar to ClosestDotSearchAgent in the previous project. The implementation involves a simple breath-first-searce that terminates at the closest food near pacman.

The effect of ghost on a pacman surges drastically when they are close one block away. Away from pacman, ghosts have little effect. The implementation involves an exponential function.

To take advantage of the pellet, every scared ghost will pose no negative influence, but on the contrary, add 25 bonus points. So the more active ghosts that are chasing after the pacman, the more it wants to take a pellet. At the very beginning of the experiment game, there are three ghosts and the pellet is very close, so the pacman is very likely to use one pellet before proceed to clear the food. However, after taking one pellet, it creates no marginal benefit to take another one, so the pacman go directly after the closest food.

Sometimes the pacman would avoid a scared ghost, because eating it produces an active ghost in the center and lose the bonus points, so if there is a choice, it would tend to leave scared ghosts alone and take as much advantage of the scared time as possible.In this mini-contest, you will apply the search algorithms and problems implemented in Project 1 to handle more difficult scenarios that include controlling multiple pacman agents and planning under time constraints.

multiagent pacman github

There is room to bring your own unique ideas, and there is no single set solution. We are very much looking forward to seeing what you come up with! Extra credit points are earned on top of the 25 points available in P1. Students that perform well in the final leaderboard will receive the following extra credit:. You only need to submit myAgents. If you import from search. The base code is nearly identical to Project 1, but with some minor modifications to include support for more than one Pacman agent.

You can download all the code and supporting files as a zip archive. Some key differences:. Files to Edit and Submit: You will fill and submit myAgents. Evaluation: Your code will be autograded to determine your final score. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder.

If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work. Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy.

The Pacman Projects

If you do, we will pursue the strongest consequences available to us. Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help.

Office hours, section, and the discussion forum are there for your support; please use them. We want these contests to be rewarding and instructional, not frustrating and demoralizing. There are a variety of layouts in the layouts directory. Agents will be exposed to a variety of maps of different sizes and amounts of food. Each agent can see the entire state of the game, such as food pellet locations, all pacman locations, etc. See the GameState section of the code for more details. Win : You win if you collect all food pellets.

Your score is the current amount of points.

multiagent pacman github

Lose : You lose if your score reaches zero. This can be caused by not finding pellets quickly enough, or spending too much time on compute. Your score for this game is zero. If your agent crashes, it automatically receives a score of zero. You should include your agents in a file of the same format as myAgents.

Your agents must be completely contained in this one file, although you may use the functions in search. The GameState in pacman. The major change to note is that many GameState methods now have an extra argument, agentIndexwhich is to identify which Pacman agent it needs.

For example, state.Pacman, now with ghosts. Minimax, Expectimax, Evaluation. In this project, you will design agents for the classic version of Pacman, including ghosts.

Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function design. The code base has not changed much from the previous project, but please start with a fresh installation, rather than intermingling files from project 1.

You can, however, use your search. If you are not sure how to submit optional files, make sure to read the submission instructions. The code for this project contains the following files, available as a zip archive. Key files to read multiAgents. This file also describes a Pacman GameState type, which you will use extensively in this project game.

Files you can ignore graphicsDisplay. You should submit this file with your code and comments. You may also submit supporting files like search. Please do not change the other files in this distribution or submit any of our original files other than multiAgents. Directions for submitting are on the course website; this assignment is submitted with the command submit p2. Evaluation: Your code will be autograded for technical correctness.

NES Tetris Lua AI playing to 999999

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. Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy.

If you copy someone else's code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don't try.

We trust you all to submit your own work only; please don't let us down. If you do, we will pursue the strongest consequences available to us. Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, section, and the newsgroup are there for your support; please use them.

If you can't make our office hours, let us know and we will schedule more. We want these projects to be rewarding and instructional, not frustrating and demoralizing. But, we don't know when or how to help unless you ask. First, play a game of classic Pacman: python pacman. The provided reflex agent code provides some helpful examples of methods that query the GameState for information.

A capable reflex agent will have to consider both food locations and ghost locations to perform well. Your agent should easily and reliably clear the testClassic layout see below for grading specifics : python pacman.

It will likely often die with 2 ghosts on the default board, unless your evaluation function is quite good. Note: you can never have more ghosts than the layout permits. Note: As features, try the reciprocal of important values such as distance to food rather than just the values themselves. Note: The evaluation function you're writing is evaluating state-action pairs; in later parts of the project, you'll be evaluating states. Options: Default ghosts are random; you can also play for fun with slightly smarter directional ghosts using -g DirectionalGhost.

If the randomness is preventing you from telling whether your agent is improving, you can use -f to run with a fixed random seed same random choices every game.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

In this project, agents are designed for the classic version of Pacman, including ghosts. Mini-max, Alpha-Beta pruning, Expectimax techniques were used to implement multi-agent pacman adversarial search. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. MultiAgent-Pacman In this project, agents are designed for the classic version of Pacman, including ghosts.

multiagent pacman github

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.Public Pastes. Lua 4 min ago. Not a member of Pastebin yet? Sign Upit unlocks many cool features! Licensing Information: Please do not distribute or publish solutions to this. You are free to use and extend these projects for educational.

John DeNero denero cs. A reflex agent chooses an action at each choice point by examining. The code below is provided as a guide.

You are welcome to change. You do not need to change this method, but you're welcome to. Just like in the previous project, getAction takes a GameState and returns. Collect legal moves and successor states. Choose one of the best actions. Design a better evaluation function here.

The evaluation function takes in the current and proposed successor. GameStates pacman. The code below extracts some useful information from the state, like the. Print out these variables to see what you're getting, then combine them. Useful information you can extract from a GameState pacman.

This default evaluation function just returns the score of the state. The score is the same one displayed in the Pacman GUI. This evaluation function is meant for use with adversarial search agents. This class provides some common elements to all of your.

Any methods defined here will be available. Please do not. Note: this is an abstract class: one that should not be instantiated.

Agent game. Your minimax agent question 2. Returns the minimax action from the current gameState using self.


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