| Display title | State–action–reward–state–action |
| Default sort key | State-action-reward-state-action |
| Page length (in bytes) | 5,819 |
| Namespace ID | 0 |
| Page ID | 300222 |
| Page content language | en - English |
| Page content model | wikitext |
| Indexing by robots | Allowed |
| Number of redirects to this page | 0 |
| Counted as a content page | Yes |
| Page image |  |
| HandWiki item ID | None |
| Edit | Allow all users (infinite) |
| Move | Allow all users (infinite) |
| Page creator | imported>MedAI |
| Date of page creation | 14:41, 6 February 2024 |
| Latest editor | imported>MedAI |
| Date of latest edit | 14:41, 6 February 2024 |
| Total number of edits | 1 |
| Recent number of edits (within past 90 days) | 0 |
| Recent number of distinct authors | 0 |
Description | Content |
Article description: (description)
This attribute controls the content of the description and og:description elements. | State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name... |
