print "#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#"
reward = Reward()
endcondition = EndCondition()
maxsteps = b
# El presupuesto inicial (igual al numero de pasos)
init_budget = maxsteps - 2
for rp in risk_profile:
print "#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#=#"
gridworld = GridWorld(xlim, ylim)
grid_states = np.asarray(gridworld.getStates())
actions = np.asarray(gridworld.getActions())
# Define al agente
agent = BudgetAgent(grid_states, actions, blim)
agent.setAgent(initial_position)
# alpha,gamma,epsilon
agent.setQLearning(0.3, 0.8, 1.0)
log = {'rewards': [], 'steps': []}
for i in range(0, episodes):
print "---------------------------------------------------------------------------------------------"
print "START EPISODE"
