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  2. Curiously recurring template pattern - Wikipedia

    en.wikipedia.org/wiki/Curiously_recurring...

    Typically, the base class template will take advantage of the fact that member function bodies (definitions) are not instantiated until long after their declarations, and will use members of the derived class within its own member functions, via the use of a cast; e.g.:

  3. Template metaprogramming - Wikipedia

    en.wikipedia.org/wiki/Template_metaprogramming

    The use of templates as a metaprogramming technique requires two distinct operations: a template must be defined, and a defined template must be instantiated.The generic form of the generated source code is described in the template definition, and when the template is instantiated, the generic form in the template is used to generate a specific set of source code.

  4. Wikipedia:List of policies and guidelines - Wikipedia

    en.wikipedia.org/wiki/Wikipedia:List_of_policies...

    This page includes a listing of policies and guidelines for English Wikipedia. Policy and guideline pages describe Wikipedia's principles and best-agreed practices ...

  5. Template (C++) - Wikipedia

    en.wikipedia.org/wiki/Template_(C++)

    Templates are a feature of the C++ programming language that allows functions and classes to operate with generic types.This allows a function or class declaration to reference via a generic variable another different class (built-in or newly declared data type) without creating full declaration for each of these different classes.

  6. typename - Wikipedia

    en.wikipedia.org/wiki/Typename

    This code looks like it should compile, but it is incorrect because the compiler does not know if T::bar is a type or a value. The reason it doesn't know is that T::bar is a "template-parameter dependent name", or "dependent name" for short, which then could represent anything named "bar" inside a type passed to foo(), which could include typedefs, enums, variables, etc.

  7. Proximal policy optimization - Wikipedia

    en.wikipedia.org/wiki/Proximal_Policy_Optimization

    Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [1] and had become the default reinforcement learning algorithm at American artificial intelligence company OpenAI. [2]