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
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
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)
