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» Reinforcement Learning: An Introduction
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ICTAI
2003
IEEE
13 years 10 months ago
Q-Concept-Learning: Generalization with Concept Lattice Representation in Reinforcement Learning
One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use al...
Marc Ricordeau
NCI
2004
185views Neural Networks» more  NCI 2004»
13 years 6 months ago
Hierarchical reinforcement learning with subpolicies specializing for learned subgoals
This paper describes a method for hierarchical reinforcement learning in which high-level policies automatically discover subgoals, and low-level policies learn to specialize for ...
Bram Bakker, Jürgen Schmidhuber
SCAI
2008
13 years 6 months ago
Fast Learning in an Actor-Critic Architecture with Reward and Punishment
Abstract. A reinforcement architecture is introduced that consists of three complementary learning systems with different generalization abilities. The ACTOR learns state-action as...
Christian Balkenius, Stefan Winberg
ICTAI
2007
IEEE
13 years 11 months ago
Multi-agent Reinforcement Learning Using Strategies and Voting
Multiagent learning attracts much attention in the past few years as it poses very challenging problems. Reinforcement Learning is an appealing solution to the problems that arise...
Ioannis Partalas, Ioannis Feneris, Ioannis P. Vlah...
SAC
2005
ACM
13 years 11 months ago
Reinforcement learning agents with primary knowledge designed by analytic hierarchy process
This paper presents a novel model of reinforcement learning agents. A feature of our learning agent model is to integrate analytic hierarchy process (AHP) into a standard reinforc...
Kengo Katayama, Takahiro Koshiishi, Hiroyuki Narih...