text: from rlpy.Domains import PST
  from rlpy.Agents import Greedy_GQ
  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, 50, 1),
      'boyan_N0': hp.loguniform("boyan_N0", np.log(1e1), np.log(1e5)),
      'initial_learn_rate': hp.loguniform("initial_learn_rate", np.log(5e-2), np.log(1))}
  
  
  def make_experiment(
          exp_id=1, path="./Results/Temp/{domain}/{agent}/{representation}/",
          lambda_=0.,
          boyan_N0=3019.313,
          initial_learn_rate=0.965830):
      opt = {}
      opt["path"] = path
      opt["exp_id"] = exp_id
      opt["max_steps"] = 500000
      opt["num_policy_checks"] = 30
      opt["checks_per_policy"] = 10
      beta_coef = 1e-6
      domain = PST(NUM_UAV=4)
      opt["domain"] = domain
      representation = IndependentDiscretization(domain)
      policy = eGreedy(representation, epsilon=0.1)
      opt["agent"] = Greedy_GQ(policy, representation,
                        discount_factor=domain.discount_factor,
                        BetaCoef=beta_coef,
                        lambda_=lambda_, initial_learn_rate=initial_learn_rate,
                        learn_rate_decay_mode="boyan", boyan_N0=boyan_N0)
      experiment = Experiment(**opt)
      return experiment
  
  if __name__ == '__main__':
      from rlpy.Tools.run import run_profiled
      run_profiled(make_experiment)
      #experiment = make_experiment(1)
      # experiment.run()
      # experiment.plot()
      # experiment.save()
  