AI engineering glossary · Reinforcement learning
What is offline reinforcement learning?
Offline reinforcement learning (also called Batch reinforcement learning): Learning a decision rule purely from a fixed dataset of previously logged interactions, with no further trial in the real world. It is valuable where experimenting is costly or unsafe, but must avoid over-trusting actions the data never covers.
Offline reinforcement learning: an example
Learn a discount-offer strategy from a year of logged offers and outcomes without sending new experimental offers.
Related terms
More Reinforcement learning terms
- On-policy
- Optimal policy
- Options framework
- Overestimation bias
- Partially observable Markov decision process
- Policy evaluation
- Policy gradient theorem
- Policy improvement
Where you learn it
Offline reinforcement learning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.