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FUZZIEEE
2007
IEEE
14 years 2 months ago
Fuzzy Approximation for Convergent Model-Based Reinforcement Learning
— Reinforcement learning (RL) is a learning control paradigm that provides well-understood algorithms with good convergence and consistency properties. Unfortunately, these algor...
Lucian Busoniu, Damien Ernst, Bart De Schutter, Ro...
ATAL
2008
Springer
13 years 9 months ago
Transfer of task representation in reinforcement learning using policy-based proto-value functions
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, ...
Eliseo Ferrante, Alessandro Lazaric, Marcello Rest...
IJCAI
2007
13 years 9 months ago
Heuristic Selection of Actions in Multiagent Reinforcement Learning
This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement Learni...
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna...
ECML
2004
Springer
14 years 1 months ago
Experiments in Value Function Approximation with Sparse Support Vector Regression
Abstract. We present first experiments using Support Vector Regression as function approximator for an on-line, sarsa-like reinforcement learner. To overcome the batch nature of S...
Tobias Jung, Thomas Uthmann
AAMAS
2007
Springer
14 years 1 months ago
Continuous-State Reinforcement Learning with Fuzzy Approximation
Abstract. Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents. There exist several convergent and consistent RL algorithms which have been intensivel...
Lucian Busoniu, Damien Ernst, Bart De Schutter, Ro...