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» Q-Decomposition for Reinforcement Learning Agents
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AGENTS
2001
Springer
14 years 5 days ago
Using background knowledge to speed reinforcement learning in physical agents
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...
Daniel G. Shapiro, Pat Langley, Ross D. Shachter
ATAL
2006
Springer
13 years 11 months ago
Improving reinforcement learning with context detection
Bruno Castro da Silva, Eduardo W. Basso, Filipo St...
AAAI
2006
13 years 9 months ago
RL-CD: Dealing with Non-Stationarity in Reinforcement Learning
Bruno Castro da Silva, Eduardo W. Basso, Ana L. C....
KCAP
2009
ACM
14 years 2 months ago
Interactively shaping agents via human reinforcement: the TAMER framework
As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without n...
W. Bradley Knox, Peter Stone
ATAL
2006
Springer
13 years 11 months ago
Learning the required number of agents for complex tasks
Coordinating agents in a complex environment is a hard problem, but it can become even harder when certain characteristics of the tasks, like the required number of agents, are un...
Sébastien Paquet, Brahim Chaib-draa