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CEC
2007
IEEE
15 years 10 months ago
Evolving neuromodulatory topologies for reinforcement learning-like problems
— Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advanta...
Andrea Soltoggio, Peter Dürr, Claudio Mattius...
135
Voted
SOCROB
2010
126views Robotics» more  SOCROB 2010»
15 years 2 months ago
Using the Interaction Rhythm as a Natural Reinforcement Signal for Social Robots: A Matter of Belief
Abstract. In this paper, we present the results of a pilot study of a human robot interaction experiment where the rhythm of the interaction is used as a reinforcement signal to le...
Antoine Hiolle, Lola Cañamero, Pierre Andry...
176
Voted
AUTOMATICA
2008
198views more  AUTOMATICA 2008»
15 years 2 months ago
Asynchronous cellular learning automata
Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in dif...
Hamid Beigy, Mohammad Reza Meybodi
138
Voted
ICML
2005
IEEE
16 years 4 months ago
Bayesian sparse sampling for on-line reward optimization
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
99
Voted
ICML
2006
IEEE
16 years 4 months ago
Relational temporal difference learning
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
Nima Asgharbeygi, David J. Stracuzzi, Pat Langley