Grid cells, place cells, and geodesic generalization for spatial reinforcement learning.
Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of...
Saved in:
| Main Authors: | Nicholas J Gustafson, Nathaniel D Daw |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-10-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002235&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A memory model of rodent spatial navigation in which place cells are memories arranged in a grid and grid cells are non-spatial
by: David E Huber
Published: (2025-05-01) -
Spatial heuristics and random spatial exploration: children, adults, and the machine coloring-in places in the grid game
by: Christiane Lange-Küttner, et al.
Published: (2025-08-01) -
String Theory Explanation of Galactic Rotation Found Using the Geodesic Constraint
by: Mark D. Roberts
Published: (2019-01-01) -
Approximating Fixed Points of Enriched Nonexpansive Mappings in Geodesic Spaces
by: Rahul Shukla, et al.
Published: (2022-01-01) -
Geodesic B-Preinvex Functions and Multiobjective Optimization Problems on Riemannian Manifolds
by: Sheng-lan Chen, et al.
Published: (2014-01-01)