The first book you read about Reinforcement Learning is likely the book Reinforcement Learning: An Introduction. In the book, dear Richard Sutton and Andrew Barto introduced reinforcement learning from the view of maximizing total Reward in very detail. The book explained reinforcement learning step by step:
Determinant > 0
Eigenvalue > 0
pivot: pivot and pivot divided by a (for the second pviot, multiply together to become determinant)
x^T A x > 0
Semi definite when it is equal to 0
2. 2D example first
Find the pivot…
Model-Free Reinforcement Learning(MFRL) become popular again after the combination of convolutional neural network and Q learning algorithm. The idea behind MFRL is using the Bellman equation to model the temporal relation between current and future.
However, the data efficiency of MFRL is a huge concern. There are two reasons that…
The learning process is a statistic process, inferencing is probabilistic. The training samples are sampled from unknown probability distributions Pr(X) and Pr(Y). The result of NN gives out the estimated distribution Pr(Y|X). Integrate out X to get Y: Pr(Y)=∫ Pr(Y|X)Pr(X) dx.
When I started to crack machine learning, I read…
All entries ≥ 0
All columns add to 1.
Eigenvalue = 1 is one of the eigenvalues. All eigenvalue ≤ 1 to guarantee to have a steady-state.
2. Find the steady-state of the Markov matrix. The power of the matrix. Similar to what happened before. …
b is perpendicular to the column space: b is projected to be a point, a 0 vector.
N(A^T) is perpendicular to the column space.
Pb = A(A^T A)^-1 A^T b . A^T * N(A^T) = 0
b in column…
no c will give…