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ICML
2002
IEEE
14 years 8 months ago
Hierarchically Optimal Average Reward Reinforcement Learning
Two notions of optimality have been explored in previous work on hierarchical reinforcement learning (HRL): hierarchical optimality, or the optimal policy in the space defined by ...
Mohammad Ghavamzadeh, Sridhar Mahadevan
ATAL
2011
Springer
12 years 7 months ago
Metric learning for reinforcement learning agents
A key component of any reinforcement learning algorithm is the underlying representation used by the agent. While reinforcement learning (RL) agents have typically relied on hand-...
Matthew E. Taylor, Brian Kulis, Fei Sha
AAAI
1993
13 years 9 months ago
Learning Semantic Grammars with Constructive Inductive Logic Programming
Automating the construction of semantic grammars is a di cult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be vi...
John M. Zelle, Raymond J. Mooney
AAAI
2010
13 years 9 months ago
Learning Simulation Control in General Game-Playing Agents
The aim of General Game Playing (GGP) is to create intelligent agents that can automatically learn how to play many different games at an expert level without any human interventi...
Hilmar Finnsson, Yngvi Björnsson
GECCO
2008
Springer
182views Optimization» more  GECCO 2008»
13 years 8 months ago
Scaling ant colony optimization with hierarchical reinforcement learning partitioning
This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. Th...
Erik J. Dries, Gilbert L. Peterson