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ICAART
2010
INSTICC
16 years 1 months ago
Complexity of Stochastic Branch and Bound Methods for Belief Tree Search in Bayesian Reinforcement Learning
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
Christos Dimitrakakis
ECAI
2006
Springer
15 years 7 months ago
Learning by Automatic Option Discovery from Conditionally Terminating Sequences
Abstract. This paper proposes a novel approach to discover options in the form of conditionally terminating sequences, and shows how they can be integrated into reinforcement learn...
Sertan Girgin, Faruk Polat, Reda Alhajj
EUSFLAT
2003
121views Fuzzy Logic» more  EUSFLAT 2003»
15 years 5 months ago
An adaptive learning algorithm for a neo fuzzy neuron
In the paper, a new optimal learning algorithm for a neo-fuzzy neuron (NFN) is proposed. The algorithm is characteristic in that it provides online tuning of not only the synaptic...
Yevgeniy Bodyanskiy, Illya Kokshenev, Vitaliy Kolo...
SIGECOM
2004
ACM
165views ECommerce» more  SIGECOM 2004»
15 years 9 months ago
Adaptive limited-supply online auctions
We study a limited-supply online auction problem, in which an auctioneer has k goods to sell and bidders arrive and depart dynamically. We suppose that agent valuations are drawn ...
Mohammad Taghi Hajiaghayi, Robert D. Kleinberg, Da...
COLT
2004
Springer
15 years 9 months ago
Online Geometric Optimization in the Bandit Setting Against an Adaptive Adversary
We give an algorithm for the bandit version of a very general online optimization problem considered by Kalai and Vempala [1], for the case of an adaptive adversary. In this proble...
H. Brendan McMahan, Avrim Blum