GitHub GitLab The Pacman Projects 2017-11-03 … If necessary, we will review and grade assignments individually to ensure that you receive due credit for your work. The evaluation function should evaluate states, rather than actions like your reflex agent evaluation function did. The main file that runs Pac-Man games. That is, compute values for features about the state that you think are important, and then combine those features by multiplying them by different values and adding the results together. The evaluation function for the pacman test in this part is already written (, The minimax values of the initial state in the. In particular, if Pac-Man perceives that he could be trapped but might escape to grab a few more pieces of food, he'll at least try. You can also play multiple games in a row with -n. Turn off graphics with -q to run lots of games quickly. Important: A single search ply is considered to be one Pacman move and all the ghosts' responses, so depth 2 search will involve Pacman and each ghost moving two times. Your minimax agent should work with any number of ghosts, so you'll have to write an algorithm that is slightly more general than what appears in the textbook. Improve the ReflexAgent in multiAgents.py to play respectably. Improve the action evaluation function evaluationFunction of ReflexAgent in multiAgents.py to play respectably. As in project 1, this project includes an autograder for you to grade your answers on your machine. You shouldn't change this function, but recognize that now we're evaluating *states* rather than actions, as we were for the reflex agent. The score is the same one displayed in the Pacman GUI. Office hours, section, and the discussion forum are there for your support; please use them. If you find yourself stuck on something, contact the course staff for help. This file also describes a Pacman. To see how the ExpectimaxAgent behaves in Pacman, run: You should now observe a more cavalier approach in close quarters with ghosts. A reflex agent chooses an action at each choice point by examining In particular, your minimax tree will have multiple min layers (one for each ghost) for every max layer. The correct implementation of minimax will lead to Pacman losing the game in some tests. You shouldn't change this function, but recognize that now we're evaluating *states* rather than actions, as we were for the reflex agent. (Indeed, alternatively, but incompatible with our autograder, would be to also allow for pruning on equality and invoke alpha-beta once on each child of the root node, but this will not match the autograder.). To simplify your code, assume you will only be running against an adversary which chooses amongst their getLegalActions uniformly at random. The reflex agent code provides some helpful examples of methods that query the GameState: A GameState object specifies the full game state, including the food, capsules, ... Write a better evaluation function for Pac-Man in the provided function betterEvaluationFunction. You will receive an addition 1 point if your agent's average score is greater than 500, or 2 points if it is greater than 1000. Office hours and piazza are there for your support; please use them. Question 5 (5 points) Write a better evaluation function for pacman in the provided function betterEvaluationFunction. Please do not change the other files in this distribution or submit any of our original files other than this file. These cheat detectors are quite hard to fool, so please don't try. The evaluation function should evaluate states, rather than actions like your reflex agent evaluation function did. A reflex agent chooses an action at each choice point by examining its alternatives via an action evaluation function. +1 if your games take on average less than 30 seconds on the autograder machine. With depth 2 search, your evaluation function should clear the smallClassic layout with two random ghosts more than half the time and still run at a reasonable rate (to get full credit, Pac-Man should be averaging around 1000 points when he's winning). In particular, if Pacman perceives that he could be trapped but might escape to grab a few more pieces of food, he'll at least try. Do not change the existing function names because this will only mess up the autograder. You can debug your implementation on small the game trees using the command: Debugging on these small and manageable test cases is recommended and will help you to find bugs quickly. Ghosts: you can never have more ghosts than the layout permits. In this project, you will design agents for the classic version of Pacman, including ghosts. Minimax and alpha-beta are great, but they both assume that you are playing against an adversary who makes optimal decisions. If you are interested, try programming a more advanced Pac-man Agent and see how well it doesn against smarter foes in a trickier maze. Pacman is always agent 0, and the agents move in order of increasing agent index. Again, your algorithm will be slightly more general than the pseudo-code in the textbook, so part of the challenge is to extend the alpha-beta pruning logic appropriately to multiple minimizer agents. The provided reflex agent code has some helpful examples of methods that query the GameState for information. Along the way, you will implement both minimax and alpha-beta search and try your hand at evaluation function design. return currentGameState. Options: Default ghosts are random; you can also play for fun with slightly smarter directional ghosts using -g DirectionalGhost. betterEvaluationFunction. Go into the directory that contains your source code. Sometimes, this is the wrong thing to do with random ghosts, but minimax agents always assume the worst: If you win at least once without timing out the autograder, you receive 1 points. A reflect agent doesn't try to look ahead to plan the best sequence … The score is the same one displayed in the Pacman GUI. We trust you all to Reflex Agent evaluates the situation right now, and what would the situation become after taking certain actions. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. 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). A capable reflex agent will have to consider both food locations and ghost locations to perform well. An evaluation function, also known as a heuristic evaluation function or static evaluation function, is a function used by game-playing programs to estimate the value or goodness of a position in the minimax and related algorithms. You shouldn't change this function, but recognize that now we're evaluating *states* rather than actions, as we were for the reflex agent. A capable reflex agent … As anyone who has ever won tic-tac-toe can tell you, this is not always the case. If you call it any more or less than necessary, the autograder will complain. archive. This is not a problem: as it is correct behaviour, it will pass the tests. Random ghosts are of course not optimal minimax agents, and so modeling them with minimax search may not be appropriate. Make a new agent that uses alpha-beta pruning to more efficiently explore the minimax tree, in AlphaBetaAgent. One way you might want to write your evaluation function is to use a linear combination of features. Getting Help: You are not alone! Score the leaves of your minimax tree with the supplied self.evaluationFunction, which defaults to scoreEvaluationFunction. If you copy someone The agent starts near the low-reward state. Question 5 (10 points) Write a better evaluation function for pacman in the provided function betterEvaluationFunction. In this assignment, you will design agents for an almost classic version of Pac-Man, which includes a ghost. When Pac-Man believes that his death is unavoidable, he will try to end the game as soon as possible because of the constant penalty for living. Get creative! Code for reading layout files and storing their contents, Parses autograder test and solution files, Directory containing the test cases for each question, Project 2 specific autograding test classes. (16 points): Reflex Agent. Design a better evaluation function here. Investigate the results of these two scenarios: You should find that your ExpectimaxAgent wins about half the time, while your AlphaBetaAgent always loses. The evaluation function should evaluate states rather than actions. Write the recurrence for pacman.py The main file that runs Pac-Man games. (3 points) Improve the ReflexAgent in multiAgents.py to play respectably. You might decide what to multiply each feature by based on how important you think it is. Inverse: As features, try the reciprocal of important values (such as distance to food) rather than just the values themselves. other submissions in the class for logical redundancy (as usual). The logic behind how the Pacman world works. Investigate the results of these two scenarios: You should find that your ExpectimaxAgent wins about half the time, while your AlphaBetaAgent always loses. ExpectimaxAgent, will no longer take the min over all ghost actions, but the expectation according to your agent's model of how the ghosts act. You should submit this file with your code and comments. You may use any tools at your disposal for evaluation, including your search code from the last project. Pacman, now with ghosts. We reserve the right to reward bonus points for clever solutions and show demonstrations in class. Don't spend too much time on this question, though, as the meat of the project lies ahead. Make sure your minimax code makes reference to these two variables where appropriate as these variables are populated in response to command line options. Pac-Man. The evaluation function for the Pacman test in this part is already written (self.evaluationFunction). We're very curious about what great ideas you have, so don't be shy. Look-ahead agents evaluate future states whereas reflex agents evaluate actions from the current state. In this project, you will design agents for the classic version of Pacman, including ghosts. Home / Uncategorised / pacman multi agent search. In particular, your minimax tree will have multiple min layers (one for each ghost) for every max layer. The evaluation function should evaluate states, rather than actions like your reflex agent evaluation function did. If you copy someone else's code and submit it with minor changes, we will know. This file describes several supporting types like AgentState, Agent, Direction, and Grid. Along the way, you will implement both minimax and expectimax search and try your hand at evaluation function … Make sure you understand why the behavior here differs from the minimax case. You should submit this file with your code and comments. Score the leaves of your minimax tree with the supplied self.evaluationFunction, which defaults to scoreEvaluationFunction. You will receive 0 points if your agent times out, or never wins. If you have problems submitting, please contact the TAs immediately. You can submit multiple times and we will grade your latest submission -- so feel free to submit a lot. Grading: the autograder will run your agent on the smallClassic layout 10 times. In other words, successor states should always be processed in the order returned by GameState.getLegalActions. The pseudo-code below represents the algorithm you should implement for this question. We want these projects to be rewarding and instructional, not frustrating and demoralizing. Write a better evaluation function for pacman in the provided function betterEvaluationFunction. If you are looking for inspiration, an interesting algorithm try Monte Carlo Tree Search. Integer division in Python truncates, so that 1/2 = 0, unlike the case with floats where 1.0/2.0 = 0.5. else's code and submit it with minor changes, we will know. Pacman's been doing well so far, but what if things got a bit more challenging? Monte Carlo Tree Search (or MCTS for short) is one of the most popular algorithms for cutting edge General Game Players. The evaluation function is a heuristic function that attempts to estimate the strength of the current board state where a bigger score is better for the given player. Now you will write an adversarial search agent in the provided MinimaxAgent class stub in multiAgents.py. submit your own work only; please don't let us down. 1. Minimax, Expectimax, (3 points) Improve the ReflexAgent in multiAgents.py to play respectably. You shouldn’t change this function, but recognize that now we’re evaluating states rather than actions, as we were for the reflex agent. Ideally, depth 3 on smallClassic should run in just a few seconds per move or faster. Minimax, AlphaBeta, Evaluation. The provided reflex … In particular, the ghosts will actively chase Pacman instead of wandering around randomly, and the maze features more twists and dead-ends, but also extra pellets to give Pacman a fighting chance. +1 for winning at least 5 times, +2 for winning all 10 times, +1 for an average score of at least 500, +2 for an average score of at least 1000 (including scores on lost games). You may use any tools at your disposal for evaluation, including your search code from the last project. The provided reflex agent code has some helpful examples of methods that query the GameState for information. 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. You must not prune on equality in order to match the set of states explored by our autograder. In order to implement an agent like this, we must consider carefully about the evaluation function. The evaluation function is typically designed to prioritize speed over accuracy; the function looks only at the current position and does not … When Pacman believes that his death is unavoidable, he will try to end the game as soon as possible because of the constant penalty for living. Now you will write an adversarial search agent in the provided MinimaxAgent class stub in multiAgents.py. Then type: As a result, the autograder will be very picky about how many times you call GameState.generateSuccessor. Make sure when you compute your averages that you use floats. You may use any tools at your disposal for evaluation, including your search code from the last project. 1. Uncategorised But, we don't know when or how to help unless you ask. The evaluation function in this part is already written (self.evaluationFunction). Write a better evaluation function for pacman in the provided function betterEvaluationFunction. detectors are quite hard to fool, so please don't try. 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