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
def get_q_experiment(case, minor=1):
""" Returns an experiment that uses Q-learning.
"""
gen = case.generators
profile = array([1.0])
maxSteps = len(profile)
if minor == 1:
alpha = 0.3 # Learning rate.
gamma = 0.99 # Discount factor
# The closer epsilon gets to 0, the more greedy and less explorative.
epsilon = 0.9
decay = 0.97
tau = 150.0 # Boltzmann temperature.
qlambda = 0.9
elif minor == 2:
alpha = 0.1 # Learning rate.
gamma = 0.99 # Discount factor
# The closer epsilon gets to 0, the more greedy and less explorative.
epsilon = 0.9
decay = 0.99
tau = 150.0 # Boltzmann temperature.
qlambda = 0.9
else:
raise ValueError
market = pyreto.SmartMarket(case, priceCap=cap, decommit=decommit,
auctionType=auctionType)
experiment = pyreto.continuous.MarketExperiment([], [], market, profile)
for g in gen[0:2]:
learner = Q(alpha, gamma)
# learner = QLambda(alpha, gamma, qlambda)
# learner = SARSA(alpha, gamma)
learner.explorer.epsilon = epsilon
learner.explorer.decay = decay
# 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)
# Passive agent.
task, agent = get_zero_task_agent(gen[2:3], market, nOffer, maxSteps)
experiment.tasks.append(task)
experiment.agents.append(agent)
return experiment
def get_q_experiment(case, minor=1):
locAdj = "ac"
nStates = 6
alpha = 0.2 # Learning rate.
gamma = 0.99 # Discount factor
# The closer epsilon gets to 0, the more greedy and less explorative.
epsilon = 0.9
decay = 0.999
Pd0 = get_pd_max(case, profile)
Pd_min = get_pd_min(case, profile)
market = pyreto.SmartMarket(case, priceCap=cap, decommit=decommit,
auctionType=auctionType,
locationalAdjustment=locAdj)
experiment = pyreto.continuous.MarketExperiment([], [], market)
portfolios, sync_cond = get_portfolios3()
for gidx in portfolios:
g = [case.generators[i] for i in gidx]
learner = Q(alpha, gamma)
learner.explorer.epsilon = epsilon
learner.explorer.decay = decay
task, agent = get_discrete_task_agent(g, market, nStates, nOffer,
markups, withholds, maxSteps, learner, Pd0, Pd_min)
print "ALL ACTIONS:", len(task.env._allActions) * nStates
experiment.tasks.append(task)
experiment.agents.append(agent)
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_zero_task_agent(passive, market, 1, maxSteps)
experiment.tasks.append(task)
experiment.agents.append(agent)
return experiment
def get_re_experiment(case, minor=1):
""" Returns an experiment that uses the Roth-Erev learning method.
"""
locAdj = "ac"
experimentation = 0.55
recency = 0.3
tau = 100.0
decay = 0.999
nStates = 3 # stateless RE?
Pd0 = get_pd_max(case, profile)
Pd_min = get_pd_min(case, profile)
market = pyreto.SmartMarket(case, priceCap=cap, decommit=decommit,
auctionType=auctionType,
locationalAdjustment=locAdj)
experiment = pyreto.continuous.MarketExperiment([], [], market)
portfolios, sync_cond = get_portfolios3()
for gidx in portfolios:
g = [case.generators[i] for i in gidx]
learner = VariantRothErev(experimentation, recency)
learner.explorer = BoltzmannExplorer(tau, decay)
task, agent = get_discrete_task_agent(g, market, nStates, nOffer,
markups, withholds, maxSteps, learner, Pd0, Pd_min)
print "ALL ACTIONS:", len(task.env._allActions) * nStates
experiment.tasks.append(task)
experiment.agents.append(agent)
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_zero_task_agent(passive, market, 1, maxSteps)
experiment.tasks.append(task)
experiment.agents.append(agent)
return experiment
decommit=decommit,
auctionType=auctionType,
locationalAdjustment=locAdj)
experiment = pyreto.continuous.MarketExperiment([], [], market)
portfolios, sync_cond = get_portfolios3()
for gidx in portfolios:
g = [case.generators[i] for i in gidx]
learner = VariantRothErev(experimentation, recency)
learner.explorer = BoltzmannExplorer(tau, decay)
task, agent = get_discrete_task_agent(g, market, nStates, nOffer,
markups, withholds, maxSteps,
learner, Pd0, Pd_min)
print "ALL ACTIONS:", len(task.env._allActions) * nStates
experiment.tasks.append(task)
experiment.agents.append(agent)
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_zero_task_agent(passive, market, 1, maxSteps)
experiment.tasks.append(task)
experiment.agents.append(agent)
return experiment
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
def get_q_experiment(case, minor=1):
""" Returns an experiment that uses Q-learning.
"""
gen = case.generators
