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» Efficient Reinforcement Learning
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126
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ILP
2003
Springer
15 years 7 months ago
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Thomas Gärtner, Kurt Driessens, Jan Ramon
114
Voted
ECAI
2008
Springer
15 years 4 months ago
Reinforcement Learning with the Use of Costly Features
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
Robby Goetschalckx, Scott Sanner, Kurt Driessens
NIPS
1996
15 years 3 months ago
Reinforcement Learning for Mixed Open-loop and Closed-loop Control
Closed-loop control relies on sensory feedback that is usually assumed to be free. But if sensing incurs a cost, it may be coste ective to take sequences of actions in open-loop m...
Eric A. Hansen, Andrew G. Barto, Shlomo Zilberstei...
119
Voted
JUCS
2007
98views more  JUCS 2007»
15 years 2 months ago
Focus of Attention in Reinforcement Learning
Abstract: Classification-based reinforcement learning (RL) methods have recently been proposed as an alternative to the traditional value-function based methods. These methods use...
Lihong Li, Vadim Bulitko, Russell Greiner
95
Voted
AI
2004
Springer
15 years 8 months ago
Multi-attribute Decision Making in a Complex Multiagent Environment Using Reinforcement Learning with Selective Perception
Abstract. Choosing between multiple alternative tasks is a hard problem for agents evolving in an uncertain real-time multiagent environment. An example of such environment is the ...
Sébastien Paquet, Nicolas Bernier, Brahim C...