def plotLSPIValues(gametype, layout, discountFactor=0.9, useTD=False, showValue=False):
# build the game
g = VGDLParser().parseGame(gametype)
g.buildLevel(layout)
# transform into an MDP and the mapping to observations
C = MDPconverter(g)
Ts, R, fMap = C.convert()
# find the the best least-squares approximation to the policy,
# given only observations, not the state information
if useTD:
# state-based
_, Tlspi = LSTD_PI_policy(fMap, Ts, R, discountFactor=discountFactor)
else:
# state-action-based
_, Tlspi = LSPI_policy(fMap, Ts, R, discountFactor=discountFactor)
# evaluate the policy
Vlspi = trueValues(Tlspi, R, discountFactor=discountFactor)
# plot those values
featurePlot((g.width, g.height), C.states, Vlspi)
if showValue:
# expected discounted reward at initial state
Vinit = Vlspi[C.initIndex()]
pylab.xlabel("V0=%.4f"%Vinit)
def plotLSPIValues(gametype, layout, discountFactor=0.9, useTD=False, showValue=False):
# build the game
g = VGDLParser().parseGame(gametype)
g.buildLevel(layout)
# transform into an MDP and the mapping to observations
C = MDPconverter(g)
Ts, R, fMap = C.convert()
# find the the best least-squares approximation to the policy,
# given only observations, not the state information
if useTD:
# state-based
_, Tlspi = LSTD_PI_policy(fMap, Ts, R, discountFactor=discountFactor)
else:
# state-action-based
_, Tlspi = LSPI_policy(fMap, Ts, R, discountFactor=discountFactor)
# evaluate the policy
Vlspi = trueValues(Tlspi, R, discountFactor=discountFactor)
# plot those values
featurePlot((g.width, g.height), C.states, Vlspi)
if showValue:
# expected discounted reward at initial state
Vinit = Vlspi[C.initIndex()]
pylab.xlabel("V0=%.4f"%Vinit)
def plotBackground(env, known=[[]]):
if len(known[0]) == 0:
from vgdl.mdpmap import MDPconverter
g = env._game
C = MDPconverter(g, env=env, verbose=False)
_, R, _ = C.convert()
size = (g.width, g.height)
known[0].append((size, C.states, R))
featurePlot(*known[0][0])
def plotBackground(env, known=[[]]):
if len(known[0]) == 0:
from vgdl.mdpmap import MDPconverter
g = env._game
C = MDPconverter(g, env=env, verbose=False)
_, R, _ = C.convert()
size = (g.width, g.height)
known[0].append((size, C.states, R))
featurePlot(*known[0][0])
def plotOptimalValues(gametype, layout, discountFactor=0.9, showValue=False):
# build the game
g = VGDLParser().parseGame(gametype)
g.buildLevel(layout)
# transform into an MDP
C = MDPconverter(g)
Ts, R, _ = C.convert()
# find the optimal policy
_, Topt = policyIteration(Ts, R, discountFactor=discountFactor)
# evaluate the policy
Vopt = trueValues(Topt, R, discountFactor=discountFactor)
# plot those values
featurePlot((g.width, g.height), C.states, Vopt, plotdirections=True)
if showValue:
# expected discounted reward at initial state
Vinit = Vopt[C.initIndex()]
pylab.xlabel("V0=%.4f" % Vinit)
def plotOptimalValues(gametype, layout, discountFactor=0.9, showValue=False):
# build the game
g = VGDLParser().parseGame(gametype)
g.buildLevel(layout)
# transform into an MDP
C = MDPconverter(g)
Ts, R, _ = C.convert()
# find the optimal policy
_, Topt = policyIteration(Ts, R, discountFactor=discountFactor)
# evaluate the policy
Vopt = trueValues(Topt, R, discountFactor=discountFactor)
# plot those values
featurePlot((g.width, g.height), C.states, Vopt, plotdirections=True)
if showValue:
# expected discounted reward at initial state
Vinit = Vopt[C.initIndex()]
pylab.xlabel("V0=%.4f"%Vinit)