# Reinforcement Learning > **Evolutionary Nexus:** [[articles/The Evolutionary Roots of Silicon Valley|The Evolutionary Roots of Silicon Valley]] locates reinforcement learning within the larger family of selection processes while preserving the differences between learning during a lifetime and biological evolution across generations. **Domain:** Machine Learning / Control / Selection **Doc Type:** Canonical Concept Node **Maturity:** Developed ## Definition **Reinforcement learning** is a computational framework in which an agent learns a policy for acting in an environment by using reward signals associated with consequences. The central objects are states or observations, actions, rewards, a policy and a value estimate over future returns. ## Evolutionary Relation Reinforcement learning and natural selection share a formal family resemblance: both differentially retain behavior or structure under an evaluative pressure. They are not identical. Biological selection changes population composition through inheritance and differential reproduction; reinforcement learning generally changes a policy within an agent or training process through reward-guided updating. The distinction matters for [[wiki/Mechanistic Intelligence|Mechanistic Intelligence]]. Calling every optimization process “Darwinian” can illuminate common structure, but it can also conceal where variation occurs, what is retained, who defines the reward and whether the optimized subject can contest the objective. ## Governance Context A reward function is a constitutional choice disguised as a scalar. It determines which outcomes become legible to the learner and which costs remain external. In a system affecting persons, reward design therefore requires provenance, audit, appeal and limits on experimentation. ## Key Insight **Reinforcement learning selects behavior under a reward signal; governance begins by asking who authored the signal and who bears the exploration cost.** ## Sources / Provenance - Sutton, Richard S., and Andrew G. Barto. _Reinforcement Learning: An Introduction_, second edition, 2018: http://incompleteideas.net/book/the-book-2nd.html ## See Also [[wiki/Objective Function|Objective Function]], [[wiki/Selection Gradient|Selection Gradient]], [[wiki/Natural Selection|Natural Selection]], [[wiki/Evolutionary Algorithms|Evolutionary Algorithms]], [[wiki/Moral Accountability Layer|Moral Accountability Layer]] ## Schedule–Loop Context [[Schedule and Loop]] interprets reinforcement learning as the mathematical junction of behaviorist consequence and cybernetic error correction. [[Reward Prediction Error]] and [[Temporal-Difference Learning]] are the core bridge terms.