def computeValueFromQValues(self, state):
"""
Returns max_action Q(state,action)
where the max is over legal actions. Note that if
there are no legal actions, which is the case at the
terminal state, you should return a value of 0.0.
"""
actions = ReinforcementAgent.getLegalActions(self, state)
if len(actions) < 1:
return 0.0
else:
## max value among all actions
max = -float("inf")
for action in actions:
val = self.getQValue(state, action)
if max < val:
max = val
return max
def computeActionFromQValues(self, state):
"""
Compute the best action to take in a state. Note that if there
are no legal actions, which is the case at the terminal state,
you should return None.
"""
actions = ReinforcementAgent.getLegalActions(self, state)
if len(actions) < 1:
return None
else:
max = -float("inf")
action = None
for a in actions:
val = self.getQValue(state, a)
if max < val:
max = val
action = a
return action
def getAction(self, state):
"""
Compute the action to take in the current state. With
probability self.epsilon, we should take a random action and
take the best policy action otherwise. Note that if there are
no legal actions, which is the case at the terminal state, you
should choose None as the action.
HINT: You might want to use util.flipCoin(prob)
HINT: To pick randomly from a list, use random.choice(list)
"""
# Pick Action
legalActions = ReinforcementAgent.getLegalActions(self, state)
if len(legalActions) == 0:
return None
else:
isRandom = util.flipCoin(self.epsilon)
if isRandom:
return random.choice(legalActions)
else:
return self.computeActionFromQValues(state)
