"""
Cart-pole balancing with iFDD+
"""

from rlpy.Domains import InfCartPoleBalance
from rlpy.Agents import Greedy_GQ, SARSA, Q_Learning
from rlpy.Representations import *
from rlpy.Policies import eGreedy
from rlpy.Experiments import Experiment
import numpy as np
from hyperopt import hp

param_space = {'discretization': hp.quniform("discretization", 5, 40, 1),
               'discover_threshold':
               hp.loguniform(
                   "discover_threshold",
                   np.log(1e-2),
                   np.log(1e1)),
               #'lambda_': hp.uniform("lambda_", 0., 1.),
               'boyan_N0': hp.loguniform("boyan_N0", np.log(1e1), np.log(1e5)),
               'initial_learn_rate': hp.loguniform("initial_learn_rate", np.log(1e-3), np.log(1))}


def make_experiment(
        exp_id=1, path="./Results/Temp/{domain}/{agent}/{representation}/",
        discover_threshold=0.013461679,
        lambda_=0.,
        boyan_N0=484.78006,
        initial_learn_rate=0.5651405,
        discretization=23.):
    opt = {}
    opt["path"] = path
    opt["exp_id"] = exp_id
    opt["max_steps"] = 50000
    opt["num_policy_checks"] = 20
    opt["checks_per_policy"] = 10
    sparsify = True
    kappa = 1e-7
    domain = InfCartPoleBalance()
    opt["domain"] = domain

    initial_rep = IndependentDiscretization(
        domain,
        discretization=discretization)
    representation = iFDD(domain, discover_threshold, initial_rep,
                          sparsify=sparsify,
                          discretization=discretization,
                          useCache=True,
                          iFDDPlus=1. - kappa)
    policy = eGreedy(representation, epsilon=0.1)
    opt["agent"] = Greedy_GQ(policy, representation,
                      discount_factor=domain.discount_factor,
                      lambda_=lambda_,
                      BetaCoef=1e-6,
                      initial_learn_rate=initial_learn_rate,
                      learn_rate_decay_mode="boyan", boyan_N0=boyan_N0)
    experiment = Experiment(**opt)
    return experiment

if __name__ == '__main__':
    experiment = make_experiment(1)
    experiment.run()
    experiment.save()
