def test_gapworld():
# Register the avatar first
vgdl.registry.register_class(RightMovingJumpingAvatar)
game = load_gapworld_game_and_level()
env = VGDLPybrainEnvironment(game, GapworldObserver(game))
task = VGDLPybrainTask(env)
mapper = vgdl.mdp.MDPConverter(task)
T, R = mapper.convert_task_to_mdp()
print('Known states:')
print(mapper.get_observations())
for action_i in range(T.shape[0]):
print('Action {}:'.format(env.action_set[action_i]))
print(T[action_i])
print('Rewards:')
print(R)
from pybrain.rl.learners.modelbased import policyIteration, trueValues
# policy is S x A
policy, optimal_T = policyIteration(T, R, discountFactor=.9)
# So this seems wrong whether we allow transitions from absorbing states
# or not, but it's a good indication
V = trueValues(optimal_T, R, discountFactor=.9)
print('Optimal policy:')
print(policy)
import ipdb
ipdb.set_trace()
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 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)
