Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers
Machine Learning - Special issue on learning in autonomous robots
Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
Machine Learning - Special issue on learning in autonomous robots
Journal of Intelligent and Robotic Systems
Machine Learning
Artificial Intelligence Review
Analysis and Design of Robot's Behavior: Towards a Methodology
EWLR-6 Proceedings of the 6th European Workshop on Learning Robots
On the Use of Option Policies for Autonomous Robot Navigation
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
A non-computationally-intensive neurocontroller for autonomous mobile robot navigation
Biologically inspired robot behavior engineering
Reinforcement learning based on local state feature learning and policy adjustment
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Introduction to multimedia and mobile agents
Knowledge propagation in a distributed omnidirectional vision system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Real-time dynamic fuzzy Q-learning and control of mobile robots
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Layered Learning for a Soccer Legged Robot Helped with a 3D Simulator
RoboCup 2007: Robot Soccer World Cup XI
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A genetic-fuzzy approach for mobile robot navigation among moving obstacles
International Journal of Approximate Reasoning
A state-cluster based Q-learning
ICNC'09 Proceedings of the 5th international conference on Natural computation
Backward Q-learning: The combination of Sarsa algorithm and Q-learning
Engineering Applications of Artificial Intelligence
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In this paper we propose a reinforcement connectionist learning architecture that allows an autonomous robot to acquire efficient navigation strategies in a few trials. Besides rapid learning, the architecture has three further appealing features. First, the robot improves its performance incrementally as it interacts with an initially unknown environment, and it ends up learning to avoid collisions even in those situations in which its sensors cannot detect the obstacles. This is a definite advantage over nonlearning reactive robots. Second, since it learns from basic reflexes, the robot is operational from the very beginning and the learning process is safe. Third, the robot exhibits high tolerance to noisy sensory data and good generalization abilities. All these features make this learning robot's architecture very well suited to real-world applications. We report experimental results obtained with a real mobile robot in an indoor environment that demonstrate the appropriateness of our approach to real autonomous robot control