def setExperiment(self, experiment):
Adapter.setExperiment(self, experiment)
if self.scaleActions:
if self.experiment.conditions['discreteActions']:
self.scaleActions = False
else:
if len(self.scaleActions) != self.experiment.conditions['actionDim']:
# TODO: use less generic exception, e.g. AdapterException
raise SystemExit('scaleActions must contain a pair of min/max values for each action dimension (%i). Only %i are given.'%(self.inConditions['actionDim'], len(scaleActions)))
for p in self.scaleActions:
if len(p) != 2:
raise SystemExit('scaleActions must contain a pair of min/max values for each action dimension. %s is not a pair. '%str(p))
if self.normalizeStates:
if self.experiment.conditions['discreteStates']:
self.normalizeStates = False
else:
if self.autoNormalization:
self.minStates = inf * ones(self.experiment.conditions['stateDim'])
self.maxStates = -inf * ones(self.experiment.conditions['stateDim'])
else:
self.minStates = array([tp[0] for tp in self.normalizeStates])
self.maxStates = array([tp[1] for tp in self.normalizeStates])
self.minReward = inf
self.maxReward = -inf
def setExperiment(self, experiment):
Adapter.setExperiment(self, experiment)
if self.stateIndices and not self.experiment.conditions['discreteStates']:
self.outConditions['stateDim'] = len(self.stateIndices)
if self.actionIndices and not self.experiment.conditions['discreteActions']:
self.outConditions['actionDim'] = len(self.actionIndices)
