code: def get_re_experiment(case, minor=1):
      
      gen = case.generators
  
      profile = array([1.0])
      maxSteps = len(profile)
      experimentation = 0.55
      recency = 0.3
      tau = 100.0
      decay = 0.99#9995
  
      market = pyreto.SmartMarket(case, priceCap=cap, decommit=decommit,
                                  auctionType=auctionType)
  
      experiment = pyreto.continuous.MarketExperiment([], [], market, profile)
  
      for g in gen[0:2]:
          #learner = RothErev(experimentation, recency)
          learner = VariantRothErev(experimentation, recency)
          learner.explorer = BoltzmannExplorer(tau, decay)
  
          task, agent = get_discrete_task_agent([g], market, nStates, nOffer,
              markups, withholds, maxSteps, learner)
  
          experiment.tasks.append(task)
          experiment.agents.append(agent)
  
      task1, agent1 = get_zero_task_agent(gen[2:3], market, nOffer, maxSteps)
      experiment.tasks.append(task1)
      experiment.agents.append(agent1)
  
      return experiment
docs: Returns an experiment that uses the Roth-Erev learning method.