Module pacai.core.featureExtractors

Feature extractors for game states.

Expand source code 
"""
Feature extractors for game states.
"""

import abc

from pacai.core.actions import Actions
from pacai.core.search import search
from pacai.student.searchAgents import AnyFoodSearchProblem

class FeatureExtractor(abc.ABC):
    """
    A class that takes a `pacai.core.gamestate.AbstractGameState` and `pacai.core.actions.Actions`,
    and returns a dict of features.
    """

    @abc.abstractmethod
    def getFeatures(self, state, action):
        """
        Returns a dict from features to counts
        Usually, the count will just be 1.0 for
        indicator functions.
        """

        pass

class IdentityExtractor(FeatureExtractor):
    def getFeatures(self, state, action):
        feats = {}
        feats[(state, action)] = 1.0

        return feats

class SimpleExtractor(FeatureExtractor):
    """
    Returns simple features for a basic reflex Pacman.
    """

    def getFeatures(self, state, action):
        # Extract the grid of food and wall locations and get the ghost locations.
        food = state.getFood()
        walls = state.getWalls()
        ghosts = state.getGhostPositions()

        features = {}
        features["bias"] = 1.0

        # Compute the location of pacman after he takes the action.
        x, y = state.getPacmanPosition()
        dx, dy = Actions.directionToVector(action)
        next_x, next_y = int(x + dx), int(y + dy)

        # Count the number of ghosts 1-step away.
        features["#-of-ghosts-1-step-away"] = sum((next_x, next_y) in
                Actions.getLegalNeighbors(g, walls) for g in ghosts)

        # If there is no danger of ghosts then add the food feature.
        if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
            features["eats-food"] = 1.0

        prob = AnyFoodSearchProblem(state, start = (next_x, next_y))
        dist = len(search.bfs(prob))
        if dist is not None:
            # Make the distance a number less than one otherwise the update will diverge wildly.
            features["closest-food"] = float(dist) / (walls.getWidth() * walls.getHeight())

        for key in features:
            features[key] /= 10.0

        return features

Classes

class FeatureExtractor (*args, **kwargs)

A class that takes a AbstractGameState and Actions, and returns a dict of features.

Expand source code 
class FeatureExtractor(abc.ABC):
    """
    A class that takes a `pacai.core.gamestate.AbstractGameState` and `pacai.core.actions.Actions`,
    and returns a dict of features.
    """

    @abc.abstractmethod
    def getFeatures(self, state, action):
        """
        Returns a dict from features to counts
        Usually, the count will just be 1.0 for
        indicator functions.
        """

        pass

Ancestors

  • abc.ABC

Subclasses

Methods

def getFeatures(self, state, action)

Returns a dict from features to counts Usually, the count will just be 1.0 for indicator functions.

Expand source code 
@abc.abstractmethod
def getFeatures(self, state, action):
    """
    Returns a dict from features to counts
    Usually, the count will just be 1.0 for
    indicator functions.
    """

    pass
class IdentityExtractor (*args, **kwargs)

A class that takes a AbstractGameState and Actions, and returns a dict of features.

Expand source code 
class IdentityExtractor(FeatureExtractor):
    def getFeatures(self, state, action):
        feats = {}
        feats[(state, action)] = 1.0

        return feats

Ancestors

Methods

def getFeatures(self, state, action)

Inherited from: FeatureExtractor.getFeatures

Returns a dict from features to counts Usually, the count will just be 1.0 for indicator functions.

Expand source code 
def getFeatures(self, state, action):
    feats = {}
    feats[(state, action)] = 1.0

    return feats
class SimpleExtractor (*args, **kwargs)

Returns simple features for a basic reflex Pacman.

Expand source code 
class SimpleExtractor(FeatureExtractor):
    """
    Returns simple features for a basic reflex Pacman.
    """

    def getFeatures(self, state, action):
        # Extract the grid of food and wall locations and get the ghost locations.
        food = state.getFood()
        walls = state.getWalls()
        ghosts = state.getGhostPositions()

        features = {}
        features["bias"] = 1.0

        # Compute the location of pacman after he takes the action.
        x, y = state.getPacmanPosition()
        dx, dy = Actions.directionToVector(action)
        next_x, next_y = int(x + dx), int(y + dy)

        # Count the number of ghosts 1-step away.
        features["#-of-ghosts-1-step-away"] = sum((next_x, next_y) in
                Actions.getLegalNeighbors(g, walls) for g in ghosts)

        # If there is no danger of ghosts then add the food feature.
        if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
            features["eats-food"] = 1.0

        prob = AnyFoodSearchProblem(state, start = (next_x, next_y))
        dist = len(search.bfs(prob))
        if dist is not None:
            # Make the distance a number less than one otherwise the update will diverge wildly.
            features["closest-food"] = float(dist) / (walls.getWidth() * walls.getHeight())

        for key in features:
            features[key] /= 10.0

        return features

Ancestors

Methods

def getFeatures(self, state, action)

Inherited from: FeatureExtractor.getFeatures

Returns a dict from features to counts Usually, the count will just be 1.0 for indicator functions.

Expand source code 
def getFeatures(self, state, action):
    # Extract the grid of food and wall locations and get the ghost locations.
    food = state.getFood()
    walls = state.getWalls()
    ghosts = state.getGhostPositions()

    features = {}
    features["bias"] = 1.0

    # Compute the location of pacman after he takes the action.
    x, y = state.getPacmanPosition()
    dx, dy = Actions.directionToVector(action)
    next_x, next_y = int(x + dx), int(y + dy)

    # Count the number of ghosts 1-step away.
    features["#-of-ghosts-1-step-away"] = sum((next_x, next_y) in
            Actions.getLegalNeighbors(g, walls) for g in ghosts)

    # If there is no danger of ghosts then add the food feature.
    if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
        features["eats-food"] = 1.0

    prob = AnyFoodSearchProblem(state, start = (next_x, next_y))
    dist = len(search.bfs(prob))
    if dist is not None:
        # Make the distance a number less than one otherwise the update will diverge wildly.
        features["closest-food"] = float(dist) / (walls.getWidth() * walls.getHeight())

    for key in features:
        features[key] /= 10.0

    return features