166 search results - page 11 / 34 » Safe exploration for reinforcement learning |
AUSAI
2005
Springer
15 years 5 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
15 years 3 months 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
16 years 15 days 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...
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AAAI
2006
15 years 1 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...
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AGENTS
2001
Springer
15 years 4 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...