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ATAL
2007
Springer
16 years 29 days ago
Multiagent learning in adaptive dynamic systems
Classically, an approach to the multiagent policy learning supposed that the agents, via interactions and/or by using preliminary knowledge about the reward functions of all playe...
Andriy Burkov, Brahim Chaib-draa
LION
2007
Springer
192views Optimization» more  LION 2007»
16 years 28 days ago
Learning While Optimizing an Unknown Fitness Surface
This paper is about Reinforcement Learning (RL) applied to online parameter tuning in Stochastic Local Search (SLS) methods. In particular a novel application of RL is considered i...
Roberto Battiti, Mauro Brunato, Paolo Campigotto
BMCBI
2010
113views more  BMCBI 2010»
15 years 7 months ago
Unifying generative and discriminative learning principles
Background: The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of differe...
Jens Keilwagen, Jan Grau, Stefan Posch, Marc Stric...
JMLR
2008
151views more  JMLR 2008»
15 years 6 months ago
Learning to Combine Motor Primitives Via Greedy Additive Regression
The computational complexities arising in motor control can be ameliorated through the use of a library of motor synergies. We present a new model, referred to as the Greedy Addit...
Manu Chhabra, Robert A. Jacobs
JAIR
2002
163views more  JAIR 2002»
15 years 6 months ago
Efficient Reinforcement Learning Using Recursive Least-Squares Methods
The recursive least-squares (RLS) algorithm is one of the most well-known algorithms used in adaptive filtering, system identification and adaptive control. Its popularity is main...
Xin Xu, Hangen He, Dewen Hu