feat: docs

multiagent/pacmanAgents.py

@@ -0,0 +1,63 @@
+# pacmanAgents.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).
+
+
+from pacman import Directions
+from game import Agent
+import random
+import game
+import util
+
+
+class LeftTurnAgent(game.Agent):
+    "An agent that turns left at every opportunity"
+
+    def getAction(self, state):
+        legal = state.getLegalPacmanActions()
+        current = state.getPacmanState().configuration.direction
+        if current == Directions.STOP:
+            current = Directions.NORTH
+        left = Directions.LEFT[current]
+        if left in legal:
+            return left
+        if current in legal:
+            return current
+        if Directions.RIGHT[current] in legal:
+            return Directions.RIGHT[current]
+        if Directions.LEFT[left] in legal:
+            return Directions.LEFT[left]
+        return Directions.STOP
+
+
+class GreedyAgent(Agent):
+    def __init__(self, evalFn="scoreEvaluation"):
+        self.evaluationFunction = util.lookup(evalFn, globals())
+        assert self.evaluationFunction != None
+
+    def getAction(self, state):
+        # Generate candidate actions
+        legal = state.getLegalPacmanActions()
+        if Directions.STOP in legal:
+            legal.remove(Directions.STOP)
+
+        successors = [(state.generateSuccessor(0, action), action)
+                      for action in legal]
+        scored = [(self.evaluationFunction(state), action)
+                  for state, action in successors]
+        bestScore = max(scored)[0]
+        bestActions = [pair[1] for pair in scored if pair[0] == bestScore]
+        return random.choice(bestActions)
+
+
+def scoreEvaluation(state):
+    return state.getScore()