from training import LinearFATraining
from learners import SARSALambda_LinFA_ReplacingTraces
task = LinearFATileCoding3456BalanceTask()
learner = SARSALambda_LinFA_ReplacingTraces(task.nactions, task.outdim)
learner._lambda = 0.95
task.discount = learner.rewardDiscount
agent = LinearFA_Agent(learner)
# The state has a huge number of dimensions, and the logging causes me to run
# out of memory. We needn't log, since learning is done online.
agent.logging = False
# TODO PyBrain says that the learning rate needs to decay, but I don't see that
# described in Randlov's paper.
# A higher number here means the learning rate decays slower.
learner.learningRateDecay = 100000
# NOTE increasing this number above from the default of 100 is what got the
# learning to actually happen, and fixed the bug/issue where the performance
# agent's performance stopped improving.
for i in np.arange(2000, 3800, 50):
theta = np.loadtxt(
'/home/fitze/Documents/agent-bicycle/data/balance_sarsalambda_linfa_replacetrace_anneal_112217H56M04S/theta_%i.dat'
% i)
learner._theta = theta
Q = learner._qValues(one_to_n(task.getBin(0, 0, 0, 0, 0), task.outdim))
pl.plot(Q, label='%s' % i)
#pl.legend()
pl.show()
print learner._greedyAction(one_to_n(task.getBin(0, 0, 0, 0, 0), task.outdim))
from environment import Environment
from tasks import LinearFATileCoding3456BalanceTask
from training import LinearFATraining
from learners import SARSALambda_LinFA_ReplacingTraces
task = LinearFATileCoding3456BalanceTask()
learner = SARSALambda_LinFA_ReplacingTraces(task.nactions, task.outdim)
learner._lambda = 0.95
task.discount = learner.rewardDiscount
agent = LinearFA_Agent(learner)
# The state has a huge number of dimensions, and the logging causes me to run
# out of memory. We needn't log, since learning is done online.
agent.logging = False
# TODO PyBrain says that the learning rate needs to decay, but I don't see that
# described in Randlov's paper.
# A higher number here means the learning rate decays slower.
learner.learningRateDecay = 100000
# NOTE increasing this number above from the default of 100 is what got the
# learning to actually happen, and fixed the bug/issue where the performance
# agent's performance stopped improving.
for i in np.arange(2000, 3800, 50):
theta = np.loadtxt('/home/fitze/Documents/agent-bicycle/data/balance_sarsalambda_linfa_replacetrace_anneal_112217H56M04S/theta_%i.dat' % i)
learner._theta = theta
Q = learner._qValues(one_to_n(task.getBin(0, 0, 0, 0, 0), task.outdim))
pl.plot(Q, label='%s' % i)
#pl.legend()
pl.show()
print learner._greedyAction(one_to_n(task.getBin(0, 0, 0, 0, 0), task.outdim))
