Implementations of A2C, DDQN, PG, CEM.
joint image-text embeddings using Canonical Correlations Analysis
Introduction to Reinforcement Learning by Sutton & Barto, Solutions to 1st Edition
Solutions to the 1st edition. Race car problem:
Convert non-negative square matrices with total support into doubly stochastic matrices.
>> import numpy as np
>> from sinkhorn_knopp import sinkhorn_knopp as skp
>> sk = skp.SinkhornKnopp()
>> P = [[.011, .15], [1.71, .1]]
>> P_ds = sk.fit(P)
>> print P_ds
[[ 0.06102561 0.93897439]
[ 0.93809928 0.06190072]]
>> print np.sum(P_ds, axis=0)
[ 0.99912489 1.00087511]
>> print np.sum(P_ds, axis=1)
[ 1., 1.]
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