def learn_policy(self):
# Initialize Q-learner.
qlearner = QLearner( \
self.state_space,
self.actions,
self.handle_action,
self.reset_training_world )
# Initialize goal states.
goal_states = []
print "Enumerating goal states..."
print self.state_space_dim
for state_index in xrange(qlearner.r_table.size):
state = numpy.unravel_index(state_index, qlearner.r_table.shape)
if state[FullTransform.StateOffset.Arrows] == World.ArrowState.Arrows_Complete:
goal_states.append(tuple(state))
print "Goal states: %d" % len(goal_states)
for goal_state in goal_states:
qlearner.set_r_value( goal_state, 100 )
#print qlearner.r_table
# Run Q-learner.
print "Total states: %d" % (qlearner.r_table.size)
qlearner.execute(goal_states, 500000, 50)
# Return policy.
return qlearner.get_policy()
def learn_policy(self):
# Initialize Q-learner.
qlearner = QLearner(self.state_space, self.actions, self.handle_action, self.reset_training_world)
# Initialize reward states.
goal_states = [(PositionTransform.HorizontalState.At + 1, PositionTransform.VerticleState.At + 1)]
for goal_state in goal_states:
qlearner.set_r_value(goal_state, 100)
# print qlearner.r_table
# Run Q-learner.
qlearner.execute(goal_states, 300, 50)
# Return policy.
return qlearner.get_policy()
def learn_policy(self):
# Initialize Q-learner.
qlearner = QLearner( \
self.state_space,
self.actions,
self.handle_action,
self.reset_training_world )
# Initialize reward states.
goal_states = [( self.state_space[0].index(World.SiteState.Useless), )]
for goal_state in goal_states:
qlearner.set_r_value( goal_state, 100 )
#print qlearner.r_table
# Run Q-learner.
qlearner.execute(goal_states, 300, 30)
# Return policy.
return qlearner.get_policy()
