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  2. Q-learning - Wikipedia

    en.wikipedia.org/wiki/Q-learning

    Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. [1]

  3. Black–Derman–Toy model - Wikipedia

    en.wikipedia.org/wiki/Black–Derman–Toy_model

    discount recursively through the tree using the rate at each node, i.e. via "backwards induction", from the time-step in question to the first node in the tree (i.e. i=0); repeat until the discounted value at the first node in the tree equals the zero-price corresponding to the given spot interest rate for the i-th time-step. Step 2.

  4. Partially observable Markov decision process - Wikipedia

    en.wikipedia.org/wiki/Partially_observable...

    A partially observable Markov decision process ( POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a sensor model (the probability ...

  5. Euler's constant - Wikipedia

    en.wikipedia.org/wiki/Euler's_constant

    The area of the blue region converges to Euler's constant. Euler's constant (sometimes called the Euler–Mascheroni constant) is a mathematical constant, usually denoted by the lowercase Greek letter gamma (γ), defined as the limiting difference between the harmonic series and the natural logarithm, denoted here by log:

  6. Kneser–Ney smoothing - Wikipedia

    en.wikipedia.org/wiki/Kneser–Ney_smoothing

    Kneser–Ney smoothing, also known as Kneser-Essen-Ney smoothing, is a method primarily used to calculate the probability distribution of n -grams in a document based on their histories. [1] It is widely considered the most effective method of smoothing due to its use of absolute discounting by subtracting a fixed value from the probability's ...

  7. Stochastic dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Stochastic_dynamic_programming

    Originally introduced by Richard E. Bellman in ( Bellman 1957 ), stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty. Closely related to stochastic programming and dynamic programming, stochastic dynamic programming represents the problem under scrutiny in the form of a Bellman ...

  8. "Hello, World!" program - Wikipedia

    en.wikipedia.org/wiki/"Hello,_World!"_program

    A "Hello, World!" program is generally a simple computer program which emits (or displays) to the screen (often the console) a message similar to "Hello, World!" while ignoring any user input. A small piece of code in most general-purpose programming languages, this program is used to illustrate a language's basic syntax.

  9. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming (including metaprogramming [70] and metaobjects ). [71] Many other paradigms are supported via extensions, including design by ...

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