def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
"""
pseudo code:
allowed function: self.getLegalActions(state)
create a counter for (state, action), value
the key of counter is state action pair.
if there is nothing learned, then it's 0.0
every time new state gets reward, ???
for k = 1 to ...:
for each state s:
for action in allActions:
for eachOutcome in transition:
immediateReward = ...
discountedFuture = ...
nextState value = immediateReward + discountedFuture
result = probs * nextState value
find the best action according to chooseAction
return that one.
use the batch version: each vk is computed from a fixed v(k-1) not updated at all
use
---
collect policy according to value/action later.
"""
"""
---but we don't need to do much in init
"""
self.qvalues = util.Counter()
self.values = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.Qvalues=dict()
self.visit=dict()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
# print args
"*** YOUR CODE HERE ***"
self.qvalues = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
#initialize a dictionary. The dictionary is indexed by a tuple and everything is initialized to 0 at the beginning
self.qValueMap= util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# Will use counter(Map) with key as State + Action, Value as value
self.Q = util.Counter()
def __init__(self, numOfTurn, numofgauss, var, lamda, gaussDim, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
self.qVal = util.Counter()
self.numofgauss = numofgauss
self.var = var
self.lamda = lamda
self.numOfTurn = numOfTurn
self.gaussDim = gaussDim
# init basis
self.basis = []
for i in range(self.numofgauss):
base = {}
base['mean'] = np.matrix( [ float(i)/float(self.numofgauss) for j in range(0,self.gaussDim)] )
base['var'] = np.matrix( np.diag([self.var for j in range(0,self.gaussDim)]) )
base['detOfVar'] = np.linalg.det(base['var']) # pre-calculate deteminant of covariance
base['invOfVar'] = np.linalg.inv(base['var']) # pre-calculate inverse of covariance
self.basis.append(base)
# init parameters
self.thetas = {}
self.phis = {}
self.labels = {}
self.state_action_num = 0
for t in range(0,self.numOfTurn):
for a in util.turnIndex2action('b','cycle_tree',t):
self.thetas[(t,a)] = np.matrix([[0.0] \
for i in range(self.numofgauss)])
self.phis[(t,a)] = [[] for i in range(self.numofgauss)]
self.labels[(t,a)] = []
self.state_action_num += 1 # count self.state_action_num
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# qValues ((state, action), Q(s,a))
self.qValues = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# maps (state, action) pairs to Q values
self.qvalues = Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
# Current state of the board
# based on whatever the agent has learnt so far.
self.qlearntVals = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# the data structure of qValues is: {state, {action, value}}
self.qValues = {}
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.qValues = util.Counter()
self.state_uses = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# initializing q vaules dictionary
self.q_values = defaultdict(util.Counter)
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
#Using uitl.Counter from util.py
self.states = util.Counter()
"*** YOUR CODE HERE ***"
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
## Create a dictionary object to record all Q_values in Counter() version
self.Q_Values=util.Counter()
def __init__(self, numOfTurn, numofgauss=5, var=0.25, lamda=0, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
self.qVal = util.Counter()
self.numofgauss = numofgauss
self.var = var
self.lamda = lamda
self.numOfTurn = numOfTurn
if self.numofgauss<=5:
self.minimumNum = 10
else:
self.minimumNum = 20
# init basis
self.basis = []
for i in range(self.numofgauss):
base = {}
base['mean'] = [ float(i)/float(self.numofgauss) for j in range(0,82)]
base['var'] = np.diag([var for j in range(0,82)])
self.basis.append(base)
# init parameters
self.thetas = {}
self.phis = {}
self.labels = {}
self.state_action_num = 0
for t in range(0,self.numOfTurn):
for a in util.turnIndex2action('b','cycle_tree',t):
self.thetas[(t,a)] = np.matrix([[0.0] \
for i in range(self.numofgauss)])
self.phis[(t,a)] = [[] for i in range(self.numofgauss)]
self.labels[(t,a)] = []
self.state_action_num += 1 # count self.state_action_num
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
self.qVals = util.Counter()
#self.rando = FixedRandom().random
"*** YOUR CODE HERE ***"
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
#print "ALPHA", self.alpha
#print "DISCOUNT", self.discount
#print "EXPLORATION", self.epsilon
self.qValues = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
#keeps track of q values
self.values = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.qTable = {}
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.values = util.Counter()
self.predicate = BlocksworldPred(self.mdp.count, self.mdp.stackNum)
self.root = QTreeNode(.01, self.mdp.count) # FIXME epsilon should passed via command line
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
# Initialized values
# Extension of dictionary
# Keeps track of counts for a set of keys
self.values = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# Just copy it from valueIterationAgents.py
# A Counter is a dict with default 0
self.values = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
#import pdb; pdb.set_trace()
# qvalues are organised as a util.Counter() of (state, action) pairs
self.qvalues = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
states = []
"*** YOUR CODE HERE ***"
# keep a Counter/Dictionary of Q-values
self.qvalues = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.QValues = util.Counter()
#self.qtype=qtype
self.environment=None
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# qValues is a dict of util.Counter()
self.qValues = {}
self.visitCount = {}
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
# En el init creamos un diccionario vacio donde
# guardar la informacion de los estados.
self.qValues = util.Counter()
def __init__(self, **args): """ You can initialize Q-values here... """ ReinforcementAgent.__init__(self, **args) self.Q = util.Counter() # Modify this value to set how many actions to choose randomly before selecting greedily self.greedy_constraint = 100 self.to_greedy = 0
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
#OUR CODE HERE
#So I guess we need to make a counter to stick Q values in
#Everything in it will be 0 because it's a counter
self.qvalues = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
#self.values = util.Counter()
self.qValues = util.Counter()
self.stateProbs = dict()
self.rewards = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
self.qValues = util.Counter(
) #similar to values in valueIterationAgents.py
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.Qvalues = defaultdict(lambda: defaultdict(float))
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
self.QVals = util.Counter()
def startEpisode(self):
ReinforcementAgent.startEpisode(self)
self.episode_epsilon = self.episode_init_epsilon
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.qValues = util.Counter()
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.statesActions = {}
def __init__(self, **args):
"You can initialize Q-values here..."
ReinforcementAgent.__init__(self, **args)
"*** YOUR CODE HERE ***"
self.qValueHash = dict()
