Open fxwderrick opened 5 years ago
Three coarse coding features vectors are exactly the same. `def compute_coarse_coding_features(self, num_states): assert num_states == 3
npoints = 600 cc_features = numpy.zeros((num_states, npoints)) x1 = numpy.linspace(-1.5, 1.5, npoints) x2 = numpy.linspace(-1.0, 2.0, npoints) x3 = numpy.linspace(-0.5, 2.5, npoints) mu1 = 0.0 mu2 = 0.5 mu3 = 1.0 sigma = 0.4 cc_features[0, :] = mlab.normpdf(x1, mu1, sigma) cc_features[1, :] = mlab.normpdf(x2, mu2, sigma) cc_features[2, :] = mlab.normpdf(x3, mu3, sigma)
` Samples and mean are shifted in same manner. Variance remains the same thus three probability vectors are exactly the same.
Three coarse coding features vectors are exactly the same. `def compute_coarse_coding_features(self, num_states): assert num_states == 3
` Samples and mean are shifted in same manner. Variance remains the same thus three probability vectors are exactly the same.