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 build(self, direction, x, y):
new_tako = tako.Tako(direction, x, y, self)
for gen in range(len(self.strand_1)):
self.strand_1[gen].read(self.strand_2[gen], new_tako)
#take care of net & make agent
new_tako.net.sortModules()
learner = ENAC()
new_tako.agent = LearningAgent(new_tako.net, learner)
return new_tako
def get_enac_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 = ENAC()
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
def createStupidAnimat(self, x, y):
f = open('neuro.net', 'r')
trained_net = pickle.load(f)
learner = ENAC()
learner._setLearningRate(0.03)
brain = BrainController(trained_net)
new_x = x + random.randint(-3, 3)
if new_x > 79:
new_x = 79
elif new_x < 0:
new_x = 0
new_y = y + random.randint(-3, 3)
if new_y > 79:
new_y = 79
elif new_y < 0:
new_y = 0
sa = StupidAnimat(new_x, new_y, brain, learner, self)
sa.brain.validate_net()
world = World(self)
task = InteractTask(world, sa)
self.stupid_animats.append(sa)
self.tasks.append(task)
# Create a case environment specifying the load profile. env = pyreto.CaseEnvironment(case, p1h) # Create an episodic cost minimisation task. task = pyreto.MinimiseCostTask(env) # Create a network for approximating the agent's policy function that maps # system demand to generator set-points.. nb = len([bus for bus in case.buses if bus.type == pylon.PQ]) ng = len([g for g in case.online_generators if g.bus.type != pylon.REFERENCE]) net = buildNetwork(nb, ng, bias=False) # Create an agent and select an episodic learner. #learner = Reinforce() learner = ENAC() #learner.gd.rprop = True ## only relevant for RP #learner.gd.deltamin = 0.0001 ##agent.learner.gd.deltanull = 0.05 ## only relevant for BP #learner.gd.alpha = 0.01 #learner.gd.momentum = 0.9 agent = LearningAgent(net, learner) # Adjust some parameters of the NormalExplorer. sigma = [50.0] * ng learner.explorer.sigma = sigma #learner.explorer.epsilon = 0.01 # default: 0.3 #learner.learningRate = 0.01 # (0.1-0.001, down to 1e-7 for RNNs)
if plotting:
from pylab import draw, ion, title, plot, figure, clf #@UnresolvedImport
ion()
# create environment
env = SimpleEnvironment()
env.setNoise(0.9)
# create task
task = MinimizeTask(env)
# create controller network (flat network)
net = buildNetwork(1, 1, bias=False)
net._setParameters(array([-11.]))
# create agent with controller and learner
agent = PolicyGradientAgent(net, ENAC())
# initialize parameters (variance)
agent.setSigma([-2.])
# learning options
agent.learner.alpha = 2.
# agent.learner.rprop = True
agent.actaspg = False
experiment = EpisodicExperiment(task, agent)
plots = zeros((1000, agent.module.paramdim+1), float)
for updates in range(1000):
agent.reset()
# training step
experiment.doEpisodes(10)
from pybrain.rl.agents import LearningAgent
import pickle
import time
# Create environment
sub_env = Environment(20, 20)
world = World(sub_env)
# Brain for the animat, we have already trained the data
f = open('neuro.net', 'r')
trained_net = pickle.load(f)
brain = BrainController(trained_net)
# Learning method we use
#learner = PolicyGradientLearner()
learner = ENAC()
learner._setLearningRate(0.2)
# Create an animat
animat = StupidAnimat(trained_net, learner, sub_env)
# Establish a task
task = InteractTask(world, animat)
brain.validate_net()
experiment = Experiment(task, animat)
while True:
experiment.doInteractions(10000)
animat.learn()
animat.reset()
brain.validate_net()
time.sleep(3)
from pybrain.rl.learners import ENAC
from pybrain.rl.experiments import EpisodicExperiment
batch=50 #number of samples per learning step
prnts=4 #number of learning steps after results are printed
epis=int(4000/batch/prnts) #number of roleouts
numbExp=10 #number of experiments
et = ExTools(batch, prnts, kind = "learner") #tool for printing and plotting
for runs in range(numbExp):
# create environment
env = CartPoleEnvironment()
# create task
task = BalanceTask(env, 200, desiredValue=None)
# create controller network
net = buildNetwork(4, 1, bias=False)
# create agent with controller and learner (and its options)
agent = LearningAgent(net, ENAC())
et.agent = agent
# create the experiment
experiment = EpisodicExperiment(task, agent)
#Do the experiment
for updates in range(epis):
for i in range(prnts):
experiment.doEpisodes(batch)
state, action, reward = agent.learner.dataset.getSequence(agent.learner.dataset.getNumSequences()-1)
et.printResults(reward.sum(), runs, updates)
et.addExps()
et.showExps()
case = Case(name="1Bus", buses=[bus1])
""" The market will clear submitted offers/bids and return dispatch info. """
mkt = SmartMarket(case)
agents = []
tasks = []
for g in bus1.generators:
""" Create an environment for each agent with an asset and a market. """
env = ParticipantEnvironment(g, mkt, n_offbids=2)
""" Create a task for the agent to achieve. """
task = ProfitTask(env)
""" Build an artificial neural network for the agent. """
net = buildNetwork(task.outdim, task.indim, bias=False, outputbias=False)
# net._setParameters(array([9]))
""" Create a learning agent with a learning algorithm. """
agent = LearningAgent(module=net, learner=ENAC())
""" Initialize parameters (variance). """
# agent.setSigma([-1.5])
""" Set learning options. """
agent.learner.alpha = 2.0
# agent.learner.rprop = True
agent.actaspg = False
# agent.disableLearning()
agents.append(agent)
tasks.append(task)
""" The Experiment will coordintate the interaction of the given agents and
their associated tasks. """
experiment = MarketExperiment(tasks, agents, mkt)
experiment.setRenderer(ExperimentRenderer())
""" Instruct the experiment to coordinate a set number of interactions. """
