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# valueIterationAgents.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).
import mdp, util
from learningAgents import ValueEstimationAgent
class ValueIterationAgent(ValueEstimationAgent):
"""
* Please read learningAgents.py before reading this.*
A ValueIterationAgent takes a Markov decision process
(see mdp.py) on initialization and runs value iteration
for a given number of iterations using the supplied
discount factor.
"""
def __init__(self, mdp, discount=0.9, iterations=100):
"""
Your value iteration agent should take an mdp on
construction, run the indicated number of iterations
and then act according to the resulting policy.
Some useful mdp methods you will use:
mdp.getStates()
mdp.getPossibleActions(state)
mdp.getTransitionStatesAndProbs(state, action)
mdp.getReward(state, action, nextState)
mdp.isTerminal(state)
"""
self.mdp = mdp
self.discount = discount
self.iterations = iterations
self.values = util.Counter() # A Counter is a dict with default 0
# Write value iteration code here
"*** YOUR CODE HERE ***"
# This initializes the Value Iteration Agent with values so that the agent can create policies
# for x number of iterations
for _ in range(self.iterations):
# make a copy of the current values
lastValues = self.values.copy()
# for every state
for state in self.mdp.getStates():
computedValues = []
# for every action
for action in self.mdp.getPossibleActions(state):
# Store the updated Q-Value
computedValue = self.computeQValueFromValues(state, action)
computedValues.append(computedValue)
if not self.mdp.isTerminal(state):
# Update the current values with the new max
lastValues[state] = max(computedValues)
# update the (now old) values with the new values we just set
self.values = lastValues.copy()
def getValue(self, state):
"""
Return the value of the state (computed in __init__).
"""
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 ***"
# This function computes the Q-Values for a specific action taken in a specific state.
calculatedValue = 0
# for each successor state and probability of that state occurring
for nextState, probability in self.mdp.getTransitionStatesAndProbs(state, action):
# This is the Value Iteration function
calculatedValue += probability * (
self.mdp.getReward(state, action, nextState)
+ self.discount * self.values[nextState]
)
return calculatedValue
def computeActionFromValues(self, state):
"""
The policy is the best action in the given state
according to the values currently stored in self.values.
You may break ties any way you see fit. Note that if
there are no legal actions, which is the case at the
terminal state, you should return None.
"""
"*** YOUR CODE HERE ***"
# This function returns the best action to take from the current values
if self.mdp.isTerminal(state):
return None
# gets all the Q-Values for every action
qValues = util.Counter()
for action in self.mdp.getPossibleActions(state):
qValues[action] = self.computeQValueFromValues(state, action)
# return the best action
return qValues.argMax()
def getPolicy(self, state):
return self.computeActionFromValues(state)
def getAction(self, state):
"Returns the policy at the state (no exploration)."
return self.computeActionFromValues(state)
def getQValue(self, state, action):
return self.computeQValueFromValues(state, action)
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