# -*- coding: utf-8 -*-

"""

Created on Wed Jun 17 10:05:47 2015

@author: pabloem

"""

import numpy as np

import random

random.seed(1985)

from agent import Tdlambda_replacing

from functionApproximator import FA_task1

from environment import Randomwalk

from exputils import performance, plot_curves

# Agent

settings = {}

settings["gamma"] = 0.99

settings["alpha"] = 1

settings["lambda_"] = 0

fa = FA_task1()

tdAgent = Tdlambda_replacing(settings, fa)

# Environment

n_states = 11

transition_prob = 0.9

walker = Randomwalk(n_states, transition_prob)

# Experiment

confg = {}

confg["episodes"] = 10

confg["independent_runs"] = 25

alpha_vect = np.arange(0, 1.6, 0.1)

lambda_vect = np.arange(0, 1.1, 0.1)

rmse = np.zeros((len(lambda_vect), len(alpha_vect)))

# Loop over the lambda and alpha parameters

for i, lambda_ in enumerate(lambda_vect):

for j, alpha in enumerate(alpha_vect):

setattr(tdAgent, "lambda_", lambda_)

setattr(tdAgent, "alpha", alpha)

rmse[i][j] = performance(tdAgent, walker, confg)

print rmse

plot_curves(alpha_vect, rmse, save=1, name="TDlambda_replacing_task1")