A reinforcement learning adaptive fuzzy controller for robots

Authors:
Chuan-Kai Lin
Affiliations:
Department of Electrical Engineering, Chinese Navel Academy, 669 Chun Hsiao Road, Kaohsiung 813, Taiwan
Venue:
Fuzzy Sets and Systems - Theme: Modeling and control
Year:
2003

Citing 5
Cited 10

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
Fuzzy inference system learning by reinforcement methods

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Approximation theory of fuzzy systems-MIMO case

IEEE Transactions on Fuzzy Systems
Multilayer neural-net robot controller with guaranteed tracking performance

IEEE Transactions on Neural Networks

H∞ reinforcement learning control of robot manipulators using fuzzy wavelet networks

Fuzzy Sets and Systems
A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems

Expert Systems with Applications: An International Journal
Exploration and exploitation balance management in fuzzy reinforcement learning

Fuzzy Sets and Systems
Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints

Fuzzy Sets and Systems
Review article: Synergizing reinforcement learning and game theory-A new direction for control

Applied Soft Computing
Approximate dynamic programming with a fuzzy parameterization

Automatica (Journal of IFAC)
Continuous-state reinforcement learning with fuzzy approximation

ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Radial basis function neural network-based adaptive critic control of induction motors

Applied Soft Computing
Statistical estimation and modified learning vector quantization based flexible fuzzy neural networks with applications

ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Adaptive critic neural networks for identification of wheeled mobile robot

ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new reinforcement learning scheme is developed for a class of serial-link robot arms. Traditional reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment. In the proposed reinforcement learning scheme, an agent is employed to collect signals from a fixed gain controller, an adaptive critic element and a fuzzy action-generating element. The action generating element is a fuzzy approximator with a set of tunable parameters, and the performance measurement mechanism sends an error metric to the adaptive critic element for generating and transferring a reinforcement learning signal to the agent. Moreover, a tuning algorithm of the proposed scheme that can guarantee both tracking performance and stability is derived from the Lyapunov stability theory. Therefore, a combination of adaptive fuzzy control and reinforcement learning scheme is also concerned with algorithms for eliminating a sequence of decisions from experience. Simulations of the proposed reinforcement adaptive fuzzy control scheme on the cart-pole balancing problem and a two-degree-of freedom (2DOF) manipulator, SCARA robot arm verify the effectiveness of our approach.