Implement p3q1 value iteration.

p3_rl/valueIterationAgents.py

@@ -43,8 +43,18 @@ class ValueIterationAgent(ValueEstimationAgent):
         self.iterations = iterations
         self.values = util.Counter() # A Counter is a dict with default 0
 
-        # Write value iteration code here
-        "*** YOUR CODE HERE ***"
+        for _ in range(iterations):
+            # update each state once
+            values_k1 = self.values.copy()
+            for state in mdp.getStates():
+                actions = mdp.getPossibleActions(state)
+                if not actions:
+                    max_action_value = self.values[state]
+                else:
+                    max_action_value = max([self.computeQValueFromValues(state, action)
+                                            for action in actions])
+                values_k1[state] = max_action_value
+            self.values = values_k1
 
 
     def getValue(self, state):
@@ -53,14 +63,16 @@ class ValueIterationAgent(ValueEstimationAgent):
         """
         return self.values[state]
 
-
     def computeQValueFromValues(self, state, action):
         """
           Compute the Q-value of action in state from the
           value function stored in self.values.
         """
-        "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+        value = 0
+        for next_state, prob in self.mdp.getTransitionStatesAndProbs(state, action):
+            reward = self.mdp.getReward(state, action, next_state)
+            value += prob * (reward + self.discount * self.values[next_state])
+        return value
 
     def computeActionFromValues(self, state):
         """
@@ -71,8 +83,12 @@ class ValueIterationAgent(ValueEstimationAgent):
           there are no legal actions, which is the case at the
           terminal state, you should return None.
         """
-        "*** YOUR CODE HERE ***"
-        util.raiseNotDefined()
+        actions = self.mdp.getPossibleActions(state)
+        if not actions:
+            return None
+        return max([(self.computeQValueFromValues(state, action), action)
+                    for action in actions])[1]
+
 
     def getPolicy(self, state):
         return self.computeActionFromValues(state)