Self‐regulating action exploration in reinforcement learning
The basic tenet of a learning process is for an agent to learn for only as much and as long as it is necessary. With reinforcement learning, the learning process is divided between exploration and exploitation. Given the complexity of the problem domain and the randomness of the learning process, th...
Saved in:
Main Authors: | TENG, Teck-Hou, TAN, Ah-hwee, TAN, Yuan-Sin |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2012
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5239 https://ink.library.smu.edu.sg/context/sis_research/article/6242/viewcontent/82448677.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Self-regulating action exploration in reinforcement learning
by: TENG, Teck-Hou, et al.
Published: (2012) -
Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
by: WANG, Peng, et al.
Published: (2018) -
Knowledge-based exploration for reinforcement learning in self-organizing neural networks
by: TENG, Teck-Hou, et al.
Published: (2012) -
Dynamic Clustering of Contextual Multi-Armed Bandits
by: NGUYEN, Trong T., et al.
Published: (2014) -
The Determinants of Knowledge Exploration and Exploitation in Corporate Venture Capital Investment
by: SHEN SIEN, GRACE
Published: (2010)