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Instinct

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

An instinct is an inherited or evolutionarily developed behavioral tendency that guides action without conventional individual learning.

An instinct is a learned or evolved behavior that eventually guides action without conscious trial and error. Repeated reinforcement can move a behavior from deliberate system-2 thought into faster system-1 behavior. section 12.3.1

Evolutionary machine learning models instincts as reusable functions:

  • Evolve separate functions for distinct reinforcement-learning environments, then add those functions as operators in a new primitive set. section 12.3.2

One expression for two environments

A function evolved for Pendulum and another evolved for Mountain Car become instinct1 and instinct2. Evolution then combines them into an agent evaluated simultaneously on both environments. section 12.3.2

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes
Explains behavioral conditioning and dual-process transfer, then implements function reuse; stops short of a complete theory of generalized digital life.
Examples
Demonstrates instinct reuse with evolved equations, Pendulum, Mountain Car, primitive sets, reward evaluation, and an expression tree.
What it assumes
Assumes familiarity with reinforcement-learning environments and evolutionary function generation while introducing instinctual learning as a conceptual model.
What you can do after
Build it from scratch by defining evolved functions, registering them as operators, and evaluating one agent across multiple environments.
How it connects
Connects instincts with reinforcement learning, evolutionary machine learning, multitask reinforcement learning, dual-process theory, and Pavlovian conditioning.
Cover of Evolutionary Deep Learning: Genetic algorithms and neural networks
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.