Book ChapterDOI
Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics
Matthew Luciw,Sohrob Kazerounian,Yulia Sandamirskaya,Gregor Schöner,Jürgen Schmidhuber +4 more
- pp 198-209
TLDR
A recent framework for integrating reinforcement learning and dynamic neural fields is extended, by using the principle of shaping, in order to reduce the search space of the learning agent.Abstract:
We present here a simulated model of a mobile Kuka Youbot which makes use of Dynamic Field Theory for its underlying perceptual and motor control systems, while learning behavioral sequences through Reinforcement Learning. Although dynamic neural fields have previously been used for robust control in robotics, high-level behavior has generally been pre-programmed by hand. In the present work we extend a recent framework for integrating reinforcement learning and dynamic neural fields, by using the principle of shaping, in order to reduce the search space of the learning agent.read more
Citations
More filters
Journal ArticleDOI
NARLE: Neurocognitive architecture for the autonomous task recognition, learning, and execution
TL;DR: Combination of the DNF and FSN frameworks in a neurocognitive architecture NARLE enables pervasive learning both on the level of individual behaviors and goal-directed sequence, contributing to the progress towards more adaptive intelligent robotic systems, capable to learn new tasks and extend their behavioral repertoire in stochastic real-world environments.
References
More filters
Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Posted Content
Reinforcement Learning: A Survey
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.