104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
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# featureExtractors.py
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# --------------------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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"Feature extractors for Pacman game states"
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from game import Directions, Actions
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import util
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class FeatureExtractor:
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def getFeatures(self, state, action):
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"""
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Returns a dict from features to counts
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Usually, the count will just be 1.0 for
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indicator functions.
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"""
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util.raiseNotDefined()
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class IdentityExtractor(FeatureExtractor):
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def getFeatures(self, state, action):
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feats = util.Counter()
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feats[(state,action)] = 1.0
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return feats
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class CoordinateExtractor(FeatureExtractor):
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def getFeatures(self, state, action):
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feats = util.Counter()
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feats[state] = 1.0
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feats['x=%d' % state[0]] = 1.0
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feats['y=%d' % state[0]] = 1.0
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feats['action=%s' % action] = 1.0
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return feats
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def closestFood(pos, food, walls):
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"""
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closestFood -- this is similar to the function that we have
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worked on in the search project; here its all in one place
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"""
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fringe = [(pos[0], pos[1], 0)]
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expanded = set()
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while fringe:
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pos_x, pos_y, dist = fringe.pop(0)
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if (pos_x, pos_y) in expanded:
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continue
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expanded.add((pos_x, pos_y))
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# if we find a food at this location then exit
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if food[pos_x][pos_y]:
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return dist
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# otherwise spread out from the location to its neighbours
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nbrs = Actions.getLegalNeighbors((pos_x, pos_y), walls)
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for nbr_x, nbr_y in nbrs:
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fringe.append((nbr_x, nbr_y, dist+1))
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# no food found
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return None
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class SimpleExtractor(FeatureExtractor):
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"""
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Returns simple features for a basic reflex Pacman:
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- whether food will be eaten
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- how far away the next food is
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- whether a ghost collision is imminent
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- whether a ghost is one step away
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"""
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def getFeatures(self, state, action):
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# extract the grid of food and wall locations and get the ghost locations
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food = state.getFood()
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walls = state.getWalls()
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ghosts = state.getGhostPositions()
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features = util.Counter()
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features["bias"] = 1.0
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# compute the location of pacman after he takes the action
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x, y = state.getPacmanPosition()
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dx, dy = Actions.directionToVector(action)
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next_x, next_y = int(x + dx), int(y + dy)
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# count the number of ghosts 1-step away
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features["#-of-ghosts-1-step-away"] = sum((next_x, next_y) in Actions.getLegalNeighbors(g, walls) for g in ghosts)
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# if there is no danger of ghosts then add the food feature
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if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
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features["eats-food"] = 1.0
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dist = closestFood((next_x, next_y), food, walls)
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if dist is not None:
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# make the distance a number less than one otherwise the update
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# will diverge wildly
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features["closest-food"] = float(dist) / (walls.width * walls.height)
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features.divideAll(10.0)
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return features
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