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Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with creation and modification of software agent's knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners.

Learning action models is important when goals change. When an agent acted for a while, it can use its accumulated knowledge about actions in the domain to make better decisions. Thus, learning action models differs from reinforcement learning. It enables reasoning about actions instead of expensive trials in the world.^[1] Action model learning is a form of inductive reasoning, where new knowledge is generated based on agent's observations. It differs from standard supervised learning in that correct input/output pairs are never presented, nor imprecise action models explicitly corrected.

Usual motivation for action model learning is the fact that manual specification of action models for planners is often a difficult, time consuming, and error-prone task (especially in complex environments).

^ Amir, Eyal; Chang, Allen (2008). "Learning Partially Observable Deterministic Action Models". Journal of Artificial Intelligence Research. 33: 349–402. arXiv:1401.3437. doi:10.1613/jair.2575. S2CID 9432224.