Abstract
This chapter reviews issues of current research in reinforcement learning theories and their neural substrates. We consider how the formal constructs of states, actions, and rewards that these theories describe can be understood to map onto counterparts experienced by biological organisms learning in the real world. In each case, this correspondence involves significant difficulties. However, elaborated theoretical accounts from computer science clarify, in each case, how to extend these theories to more realistic circumstances while still preserving the core prediction error-driven learning mechanism that has been prominent in neuroeconomic accounts.
Original language | English (US) |
---|---|
Title of host publication | Neuroeconomics |
Subtitle of host publication | Decision Making and the Brain: Second Edition |
Publisher | Elsevier Inc. |
Pages | 299-320 |
Number of pages | 22 |
ISBN (Print) | 9780124160088 |
DOIs | |
State | Published - Sep 2013 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Neuroscience
Keywords
- Dopamine
- Hierarchical reinforcement learning
- Reinforcement learning
- Uncertainty