def testValueBased(self):
""" Test value-based learner.
"""
mkt = SmartMarket(self.case)
exp = MarketExperiment([], [], mkt)
for g in self.case.generators:
env = DiscreteMarketEnvironment([g], mkt)
dim_state, num_actions = (10, 10)
exp.tasks.append(ProfitTask(env, dim_state, num_actions))
module = ActionValueTable(dim_state, num_actions)
module.initialize(1.0)
# module = ActionValueNetwork(dimState=1, numActions=4)
learner = SARSA() #Q() QLambda()
# learner.explorer = BoltzmannExplorer() # default is e-greedy.
exp.agents.append(LearningAgent(module, learner))
for _ in range(1000):
exp.doInteractions(24) # interact with the env in batch mode
for agent in exp.agents:
agent.learn()
agent.reset()
def testValueBased(self):
""" Test value-based learner.
"""
mkt = SmartMarket(self.case)
exp = MarketExperiment([], [], mkt)
for g in self.case.generators:
env = DiscreteMarketEnvironment([g], mkt)
dim_state, num_actions = (10, 10)
exp.tasks.append(ProfitTask(env, dim_state, num_actions))
module = ActionValueTable(dim_state, num_actions)
module.initialize(1.0)
# module = ActionValueNetwork(dimState=1, numActions=4)
learner = SARSA() #Q() QLambda()
# learner.explorer = BoltzmannExplorer() # default is e-greedy.
exp.agents.append(LearningAgent(module, learner))
for _ in range(1000):
exp.doInteractions(24) # interact with the env in batch mode
for agent in exp.agents:
agent.learn()
agent.reset()
""" 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. """
experiment.doInteractions(3)
# for t in ("state", "action", "reward"):
# fileName = "%s-%s.table" % (a.name, t)
# tmp_name = join(tableDir, fileName)
## tmpfd, tmpName = tempfile.mkstemp(".table", prefix, tableDir)
## os.close(tmpfd) # gets deleted
# fd = file(tmpName, "w+b")
# fd.write("# %s %s data - %s\n" % (a.name, t, timestr))
# fd.close()
# tableMap[t][a.name] = tmpName
# Execute interactions with the environment in batch mode.
t0 = time.time()
x = 0
batch = 2
while x <= 1000:
experiment.doInteractions(batch)
for i, agent in enumerate(experiment.agents):
s, a, r = agent.history.getSequence(agent.history.getNumSequences() -
1)
pl.addData(i, x, scipy.mean(a))
pl2.addData(i, x, scipy.mean(r))
action, reward = agentMap[agent.name]
agentMap[agent.name] = (scipy.r_[action,
a.flatten()], scipy.r_[reward,
r.flatten()])
# for n, seq in (("state", s), ("action", a), ("reward", r)):
# tmpName = tableMap[n][agent.name]
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. """
experiment.doInteractions(3)
# for t in ("state", "action", "reward"):
# fileName = "%s-%s.table" % (a.name, t)
# tmp_name = join(tableDir, fileName)
## tmpfd, tmpName = tempfile.mkstemp(".table", prefix, tableDir)
## os.close(tmpfd) # gets deleted
# fd = file(tmpName, "w+b")
# fd.write("# %s %s data - %s\n" % (a.name, t, timestr))
# fd.close()
# tableMap[t][a.name] = tmpName
# Execute interactions with the environment in batch mode.
t0 = time.time()
x = 0
batch = 2
while x <= 1000:
experiment.doInteractions(batch)
for i, agent in enumerate(experiment.agents):
s,a,r = agent.history.getSequence(agent.history.getNumSequences() - 1)
pl.addData(i, x, scipy.mean(a))
pl2.addData(i, x, scipy.mean(r))
action, reward = agentMap[agent.name]
agentMap[agent.name] = (scipy.r_[action, a.flatten()],
scipy.r_[reward, r.flatten()])
# for n, seq in (("state", s), ("action", a), ("reward", r)):
# tmpName = tableMap[n][agent.name]
# fd = file(tmpName, "a+b")
# for i in range(batch):
