166 search results - page 11 / 34 » Safe exploration for reinforcement learning |
AUSAI
2005
Springer
14 years 2 months ago
2005
Springer
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
COLT
1994
Springer
14 years 20 days ago
1994
Springer
Realistic domains for learning possess regularities that make it possible to generalize experience across related states. This paper explores an environment-modeling framework tha...
ICML
2006
IEEE
14 years 9 months ago
2006
IEEE
When the transition probabilities and rewards of a Markov Decision Process are specified exactly, the problem can be solved without any interaction with the environment. When no s...
AAAI
2006
13 years 10 months ago
2006
My research attempts to address on-line action selection in reinforcement learning from a Bayesian perspective. The idea is to develop more effective action selection techniques b...
AGENTS
2001
Springer
14 years 1 months ago
2001
Springer
This paper describes Icarus, an agent architecture that embeds a hierarchical reinforcement learning algorithm within a language for specifying agent behavior. An Icarus program e...