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» Learning Evaluation Functions for Large Acyclic Domains
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GECCO
2006
Springer
133views Optimization» more  GECCO 2006»
15 years 1 months ago
On-line evolutionary computation for reinforcement learning in stochastic domains
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
Shimon Whiteson, Peter Stone
BMCBI
2006
78views more  BMCBI 2006»
14 years 9 months ago
An evaluation of human protein-protein interaction data in the public domain
Background: Protein-protein interaction (PPI) databases have become a major resource for investigating biological networks and pathways in cells. A number of publicly available re...
Suresh Mathivanan, Balamurugan Periaswamy, T. K. B...
AAAI
2006
14 years 11 months ago
Using Homomorphisms to Transfer Options across Continuous Reinforcement Learning Domains
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a mor...
Vishal Soni, Satinder P. Singh
82
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CORR
2010
Springer
152views Education» more  CORR 2010»
14 years 9 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
ACML
2009
Springer
15 years 2 months ago
Learning Algorithms for Domain Adaptation
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
Manas A. Pathak, Eric Nyberg