Abstract
Reinforcement learning is one of the major strands of current computational intelligence: it is used to enable an agent to explore an environment in order to ascertain the best actions in that environment. Genetic programming is a method to evolve programs and given the similarity between genetic algorithms and reinforcement learning, it is perhaps surprising that so little attention has been given to using reinforcement learning to identify useful programs. This paper makes a start on this task by investigating using reinforcement learning methods for function approximation.
| Original language | English |
|---|---|
| Title of host publication | 12th UK Workshop on Computational Intelligence (UKCI), 2012 |
| Publisher | IEEE |
| Pages | 1-5 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-4673-4391-6 |
| DOIs | |
| Publication status | Published - 1 Sept 2012 |
Keywords
- function approximation
- genetic algorithms
- learning (artificial intelligence)
- computational intelligence
- genetic programming
- reinforcement learning
- reinforcement programming
- Equations
- Function approximation
- Genetic programming
- Learning
- Mathematical model
- Programming profession