Returns an experiment that uses the Roth-Erev learning method.

def get_re_experiment(case, minor=1):
    """ Returns an experiment that uses the Roth-Erev learning method.
    """
    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