Reinforcement Learning
Also called RL
Reinforcement learning trains an agent’s behavior using reward signals from interaction. The objective concerns expected cumulative reward, so an action’s value can depend on consequences that occur later.
[Hugging Face]In practice · hypothetical example
A navigation policy receives feedback for reaching a destination while avoiding costly detours.
[Hugging Face]A little deeper
The learner observes an environment, selects actions, and receives feedback. The reward definition shapes what is optimized and may differ from what people actually want. [Hugging Face]
A common mix-up
RL always copies a correct action supplied for each step.
Learning can use rewards without a target action for every situation. [Hugging Face]
Can an action matter because of a later reward?
Sources & editorial notes
Evidence: supported. Primary-source support for this scoped entry; publication approved by the project owner.
- The Reinforcement Learning Framework ↗ (opens in new tab)Hugging Face · Publication date unknown
Relevant section: The RL process; Reward hypothesis
Last editorial review: 2026-09-13 by project-owner.
First observed in this corpus: Unknown.
Revision history
Revision 2 · Created 2026-09-13 · Updated 2026-09-13
Project owner approved the current content for publication. Existing evidence scope and limitations remain applicable.