Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Towards a theory of emergent functionality
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Intelligent behaviour in animals and robots
Intelligent behaviour in animals and robots
Operant conditioning in skinnerbots
Adaptive Behavior - Special issue on environment structure and behavior
Escape, avoidance, and imitation: a neural network approach
Adaptive Behavior
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Neural Networks - Special issue on neural control and robotics: biology and technology
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A novel hybrid learning technique applied to a self-learning multi-robot system
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Self-learning fuzzy logic controllers for pursuit-evasion differential games
Robotics and Autonomous Systems
Emergence of safe behaviours with an intrinsic reward
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Bio-inspired navigation of mobile robots
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
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Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.