def __init__(self, module, learner = None):
LearningAgent.__init__(self, module, learner)
# exploration module (linear flat network)
self.explorationmodule = buildNetwork(self.indim, self.outdim, bias=False)
# state dependent exploration layer
self.explorationlayer = StateDependentLayer(self.outdim, self.explorationmodule, 'explore')
# add exploration layer to top of network through identity connection
out = self.module.outmodules.pop()
self.module.addOutputModule(self.explorationlayer)
self.module.addConnection(IdentityConnection(out, self.module['explore'], self.module))
self.module.sortModules()
# tell learner the new module
self.learner.setModule(self.module)
# add the log likelihood (loglh) to the dataset and link it to the others
self.history.addField('loglh', self.module.paramdim)
self.history.link.append('loglh')
self.loglh = None
# if this flag is set to True, random weights are drawn after each reward,
# effectively acting like the vanilla policy gradient alg.
self.actaspg = False
def __init__(self, module, learner = None):
assert isinstance(module, FeedForwardNetwork)
assert len(module.outmodules) == 1
LearningAgent.__init__(self, module, learner)
# create gaussian layer
self.explorationlayer = GaussianLayer(self.outdim, name='gauss')
self.explorationlayer.setSigma([-2] * self.outdim)
# add gaussian layer to top of network through identity connection
out = self.module.outmodules.pop()
self.module.addOutputModule(self.explorationlayer)
self.module.addConnection(IdentityConnection(out, self.module['gauss']))
self.module.sortModules()
# tell learner the new module
self.learner.setModule(self.module)
# add the log likelihood (loglh) to the dataset and link it to the others
self.history.addField('loglh', self.module.paramdim)
self.history.link.append('loglh')
self.loglh = None
def __init__(self, module, learner=None):
assert isinstance(module, FeedForwardNetwork)
assert len(module.outmodules) == 1
LearningAgent.__init__(self, module, learner)
# create gaussian layer
self.explorationlayer = GaussianLayer(self.outdim, name='gauss')
self.explorationlayer.setSigma([-2] * self.outdim)
# add gaussian layer to top of network through identity connection
out = self.module.outmodules.pop()
self.module.addOutputModule(self.explorationlayer)
self.module.addConnection(IdentityConnection(out,
self.module['gauss']))
self.module.sortModules()
# tell learner the new module
self.learner.setModule(self.module)
# add the log likelihood (loglh) to the dataset and link it to the others
self.history.addField('loglh', self.module.paramdim)
self.history.link.append('loglh')
self.loglh = None
def __init__(self, indim, outdim):
LearningAgent.__init__(self, buildNetwork(indim, outdim))
def __init__(self, indim, outdim):
LearningAgent.__init__(self, buildNetwork(indim, outdim))
def __init__(self, module, learner):
LearningAgent.__init__(self, module, learner)
self.epsilon = 0.5
self.epsilondecay = 0.9999
