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Snippet for toyCarIRL, blog usage, not executable
def policyListUpdater(self, W, i): #add the policyFE list and differences
tempFE = self.getRLAgentFE(W, i) # get feature expectations of a new policy respective to the input weights
hyperDistance = np.abs(np.dot(W, np.asarray(self.expertPolicy)-np.asarray(tempFE))) #hyperdistance = t
self.policiesFE[hyperDistance] = tempFE
return hyperDistance # t = (weights.tanspose)*(expert-newPolicy)
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