This presentation gives a high level idea on the working of reinforcement learning and the general settings associated with it. Mainly this presentation presents the algorithms which are present in the reinforcement learning.

1. Reinforcement Learning

2. Algorithms

24. Thompson Sampling Algorithm

25. Beta
Distribution
A Beta distribution is a type of probability
distribution. This distribution represents a family
of probabilities and is a versatile way to
represent outcomes for percentages or
proportions.
The beta distribution is a continuous distribution
defined by two shape parameters. The
distribution can take on different shapes
depending on the values of the two parameters.
The beta distribution is also used in Bayesian
statistics, for example, as the prior distribution of
a binomial probability.

29. Both Shapes equal 1
The beta distribution is the uniform
distribution.
Both Shapes less than 1
The beta distribution is U-shaped
Both Shapes same & >1
The distribution is symmetric.
First Shape is Greater than Second Shape
The beta distribution is skewed to the left.
First Shape is Less than Second Shape
The beta distribution is skewed to the right.