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» Hierarchically Optimal Average Reward Reinforcement Learning
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TSMC
2002
69views more  TSMC 2002»
13 years 7 months ago
A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environm
An extended algorithm of the relative reward strength algorithm is proposed. It is shown that the proposed algorithm ensures the convergence with probability 1 to the optimal path ...
Norio Baba, Yoshio Mogami
PRIMA
2009
Springer
14 years 2 months ago
Recursive Adaptation of Stepsize Parameter for Non-stationary Environments
In this article, we propose a method to adapt stepsize parameters used in reinforcement learning for dynamic environments. In general reinforcement learning situations, a stepsize...
Itsuki Noda
ALT
2006
Springer
14 years 4 months ago
Asymptotic Learnability of Reinforcement Problems with Arbitrary Dependence
We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions. The task for an age...
Daniil Ryabko, Marcus Hutter
ICAC
2005
IEEE
14 years 1 months ago
Self-Optimizing Architecture for QoS Provisioning in Differentiated Services
This paper presents a scalable and self-optimizing architecture for Quality-of-Service (QoS) provisioning in the Differentiated Services (DiffServ) framework. The proposed archite...
Daniel Yagan, Chen-Khong Tham
GECCO
2006
Springer
198views Optimization» more  GECCO 2006»
13 years 11 months ago
Reward allotment in an event-driven hybrid learning classifier system for online soccer games
This paper describes our study into the concept of using rewards in a classifier system applied to the acquisition of decision-making algorithms for agents in a soccer game. Our a...
Yuji Sato, Yosuke Akatsuka, Takenori Nishizono