Sciweavers

Share
10 search results - page 1 / 2
» Reinforcement Learning Hierarchical Neuro-Fuzzy Politree Mod...
Sort
View
HIS
2004
9 years 24 days ago
Reinforcement Learning Hierarchical Neuro-Fuzzy Politree Model for Control of Autonomous Agents
: This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence...
Karla Figueiredo, Marley B. R. Vellasco, Marco Aur...
ATAL
2015
Springer
3 years 7 months ago
Monte Carlo Hierarchical Model Learning
Reinforcement learning (RL) is a well-established paradigm for enabling autonomous agents to learn from experience. To enable RL to scale to any but the smallest domains, it sary ...
Jacob Menashe, Peter Stone
ABIALS
2008
Springer
9 years 1 months ago
Multiscale Anticipatory Behavior by Hierarchical Reinforcement Learning
Abstract. In order to establish autonomous behavior for technical systems, the well known trade-off between reactive control and deliberative planning has to be considered. Within ...
Matthias Rungger, Hao Ding, Olaf Stursberg
AAAI
2010
9 years 26 days ago
The Model-Based Approach to Autonomous Behavior: A Personal View
The selection of the action to do next is one of the central problems faced by autonomous agents. In AI, three approaches have been used to address this problem: the programming-b...
Hector Geffner
ATAL
2010
Springer
9 years 16 days ago
Self-organization for coordinating decentralized reinforcement learning
Decentralized reinforcement learning (DRL) has been applied to a number of distributed applications. However, one of the main challenges faced by DRL is its convergence. Previous ...
Chongjie Zhang, Victor R. Lesser, Sherief Abdallah
books