enter reinforcement

reinforcement/featureExtractors.py

@@ -0,0 +1,103 @@
+# featureExtractors.py
+# --------------------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+#
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+"Feature extractors for Pacman game states"
+
+from game import Directions, Actions
+import util
+
+class FeatureExtractor:
+    def getFeatures(self, state, action):
+        """
+          Returns a dict from features to counts
+          Usually, the count will just be 1.0 for
+          indicator functions.
+        """
+        util.raiseNotDefined()
+
+class IdentityExtractor(FeatureExtractor):
+    def getFeatures(self, state, action):
+        feats = util.Counter()
+        feats[(state,action)] = 1.0
+        return feats
+
+class CoordinateExtractor(FeatureExtractor):
+    def getFeatures(self, state, action):
+        feats = util.Counter()
+        feats[state] = 1.0
+        feats['x=%d' % state[0]] = 1.0
+        feats['y=%d' % state[0]] = 1.0
+        feats['action=%s' % action] = 1.0
+        return feats
+
+def closestFood(pos, food, walls):
+    """
+    closestFood -- this is similar to the function that we have
+    worked on in the search project; here its all in one place
+    """
+    fringe = [(pos[0], pos[1], 0)]
+    expanded = set()
+    while fringe:
+        pos_x, pos_y, dist = fringe.pop(0)
+        if (pos_x, pos_y) in expanded:
+            continue
+        expanded.add((pos_x, pos_y))
+        # if we find a food at this location then exit
+        if food[pos_x][pos_y]:
+            return dist
+        # otherwise spread out from the location to its neighbours
+        nbrs = Actions.getLegalNeighbors((pos_x, pos_y), walls)
+        for nbr_x, nbr_y in nbrs:
+            fringe.append((nbr_x, nbr_y, dist+1))
+    # no food found
+    return None
+
+class SimpleExtractor(FeatureExtractor):
+    """
+    Returns simple features for a basic reflex Pacman:
+    - whether food will be eaten
+    - how far away the next food is
+    - whether a ghost collision is imminent
+    - whether a ghost is one step away
+    """
+
+    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 = util.Counter()
+
+        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
+
+        dist = closestFood((next_x, next_y), food, walls)
+        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.width * walls.height)
+        features.divideAll(10.0)
+        return features