Exemplo n.º 1
0
def get_enac_experiment(case, minor=1):
    gen = case.generators

    profile = array([1.0, 1.0])
    maxSteps = len(profile)
    initalSigma = 0.0
    sigmaOffset = -4.0

    if minor == 1:
        decay = 0.999
        learningRate = 0.01 # (0.1-0.001, down to 1e-7 for RNNs, default: 0.1)
    elif minor == 2:
        decay = 0.997
        learningRate = 0.005
    elif minor == 3:
        decay = 0.999
        learningRate = 0.05
    elif minor == 4:
        decay = 0.999
        learningRate = 0.005
    else:
        raise ValueError, "Invalid minor version: %d" % minor

    market = pyreto.SmartMarket(case, priceCap=cap, decommit=decommit,
                                auctionType=auctionType)
    experiment = pyreto.continuous.MarketExperiment([], [], market, profile)

    for g in gen[0:2]:
        learner = ENAC()
#        learner = Reinforce()
#        learner.gd.rprop = False
        # only relevant for BP
        learner.learningRate = learningRate
#        learner.gd.alpha = 0.0001
    #    learner.gd.alphadecay = 0.9
    #    learner.gd.momentum = 0.9
        # only relevant for RP
    #    learner.gd.deltamin = 0.0001


        task, agent = get_continuous_task_agent([g], market, nOffer, markupMax,
                                                withholdMax, maxSteps, learner)

        learner.explorer = ManualNormalExplorer(agent.module.outdim,
                                                initalSigma, decay,
                                                sigmaOffset)

        experiment.tasks.append(task)
        experiment.agents.append(agent)

    # Passive agent.
    task, agent = get_neg_one_task_agent(gen[2:3], market, nOffer, maxSteps)
    experiment.tasks.append(task)
    experiment.agents.append(agent)

    return experiment
Exemplo n.º 2
0
def get_reinforce_experiment(case):

    locAdj = "ac"
    initalSigma = 0.0
    sigmaOffset = -5.0
    decay = 0.995
    learningRate = 0.005

    market = pyreto.SmartMarket(case, priceCap=cap, decommit=decommit,
                                auctionType=auctionType,
                                locationalAdjustment=locAdj)


    experiment = \
        pyreto.continuous.MarketExperiment([], [], market, branchOutages=None)

    portfolios, sync_cond = get_portfolios3()

    for gidx in portfolios:
        g = [case.generators[i] for i in gidx]

        learner = Reinforce()
        learner.learningRate = learningRate

        task, agent = get_continuous_task_agent(g, market, nOffer, markupMax,
                                                withholdMax, maxSteps, learner)

        learner.explorer = ManualNormalExplorer(agent.module.outdim,
                                                initalSigma, decay,
                                                sigmaOffset)

        experiment.tasks.append(task)
        experiment.agents.append(agent)

    # Have an agent bid at marginal cost (0.0) for the sync cond.
    passive = [case.generators[i] for i in sync_cond]
    passive[0].p_min = 0.001 # Avoid invalid offer withholding.
    passive[0].p_max = 0.002
    task, agent = get_neg_one_task_agent(passive, market, 1, maxSteps)
    experiment.tasks.append(task)
    experiment.agents.append(agent)

    return experiment
Exemplo n.º 3
0
                                auctionType=auctionType,
                                locationalAdjustment=locAdj)


    experiment = \
        pyreto.continuous.MarketExperiment([], [], market, branchOutages=None)

    portfolios, sync_cond = get_portfolios3()

    for gidx in portfolios:
        g = [case.generators[i] for i in gidx]

        learner = Reinforce()
        learner.learningRate = learningRate

        task, agent = get_continuous_task_agent(g, market, nOffer, markupMax,
                                                withholdMax, maxSteps, learner)

        learner.explorer = ManualNormalExplorer(agent.module.outdim,
                                                initalSigma, decay,
                                                sigmaOffset)

        experiment.tasks.append(task)
        experiment.agents.append(agent)

    # Have an agent bid at marginal cost (0.0) for the sync cond.
    passive = [case.generators[i] for i in sync_cond]
    passive[0].p_min = 0.001  # Avoid invalid offer withholding.
    passive[0].p_max = 0.002
    task, agent = get_neg_one_task_agent(passive, market, 1, maxSteps)
    experiment.tasks.append(task)
    experiment.agents.append(agent)