Paper Info
Title
A Novel Artificial Hydrocarbon Networks Based Value Function Approximation in Hierarchical Reinforcement Learning.
Abstract
Reinforcement learning aims to solve the problem of learning optimal or near-optimal decision-making policies for a given domain problem. However, it is known that increasing the dimensionality of the input space (i.e. environment) will increase the complexity for the learning algorithms, falling into the curse of dimensionality. Value function approximation and hierarchical reinforcement learning have been two different approaches proposed to alleviate reinforcement learning from this illness. In that sense, this paper proposes a new value function approximation using artificial hydrocarbon networks - a supervised learning method inspired on chemical carbon networks-with regularization at each subtask in a hierarchical reinforcement learning framework. Comparative results using a greedy sparse value function approximation over the MAXQ hierarchical method was computed, proving that artificial hydrocarbon networks improves accuracy and efficiency on the value function approximation.
Year
DOI
Venue
2016
10.1007/978-3-319-62428-0_18
ADVANCES IN SOFT COMPUTING, MICAI 2016, PT II
Keywords
Field
DocType
Reinforcement learning,Machine learning,Artificial organic networks,Regularization
Pattern recognition,Computer science,Curse of dimensionality,Supervised learning,Bellman equation,Regularization (mathematics),Artificial intelligence,Soft computing,Machine learning,Reinforcement learning
Conference
Volume
ISSN
Citations 
10062
0302-9743
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Hiram E. Ponce12613.63