Paper Info
Title
Methods for acceleration of learning process of Reinforcement Learning Neuro-Fuzzy Hierarchical Politree model
Abstract
This paper presents two methods for accelerating the learning process of Reinforcement Learning - Neuro-Fuzzy Hierarchical Politree model (RL-NFHP): policy Q-DC-Roulette and early stopping. This model is used to provide an agent with intelligence, making it autonomous, due to the capacity of ratiocinate (infer actions) and learning, acquired knowledge through interaction with the environment. The characteristics of the RL-NFHP allow the agent to learn automatically its structure and action for each state. The RL-NFHP model was evaluated in an application benchmark known in the area of autonomous agents: car mountain problem. The results demonstrate the acceleration of learning process and the potential of this model, which works without any prior information, such as number of rules, rules of specification, or number of partitions that the input space should possess.
Year
DOI
Venue
2010
10.1109/AIS.2010.5547027
Autonomous and Intelligent Systems
Keywords
Field
DocType
fuzzy set theory,learning (artificial intelligence),trees (mathematics),autonomous agents,car mountain problem,early stopping model,learning process acceleration,neuro-fuzzy hierarchical Politree model,policy Q-DC-Roulette model,reinforcement learning,Automatic Learning,Learning,Neuro-Fuzzy Systems,Reinforcement Learning
Robot learning,Autonomous agent,Instance-based learning,Semi-supervised learning,Computer science,Q-learning,Unsupervised learning,Artificial intelligence,Machine learning,Reinforcement learning,Learning classifier system
Conference
ISBN
Citations 
PageRank 
978-1-4244-7104-1
1
0.35
References 
Authors
2
3
Name
Order
Citations
PageRank
Fabio Martins110.35
Karla Figueiredo2306.53
Marley Maria Bernardes Rebuzzi Vellasco36611.93