A possibility for implementing curiosity and boredom in model-building neural controllers
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Motivated Behavior for Goal Adoption
Selected Papers from the 4th Australian Workshop on Distributed Artificial Intelligence, Multi-Agent Systems: Theories, Languages, and Applications
Motivated reinforcement learning for non-player characters in persistent computer game worlds
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Modeling motivation for adaptive nonplayer characters in dynamic computer game worlds
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Modeling Behavior Cycles as a Value System for Developmental Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Reinforcement learning approach to multi-stage decision making problems with changes in action sets
Artificial Life and Robotics
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This article presents a computational model of motivation for learning agents to achieve adaptive, multitask learning in complex, dynamic environments. Motivation is modeled as an attention focus mechanism to extend existing learning algorithms to environments in which tasks cannot be completely predicted prior to learning. Two agent models are presented for motivated reinforcement learning and motivated supervised learning, which incorporate this model of motivation. The formalisms used to define these agent models further allow the definition of consistent metrics for evaluating motivated learning agent models. The article concludes with a demonstration of the motivated reinforcement learning agent model that uses novelty and interest as the motivation function. The model is evaluated using the new metrics. Results show that motivated reinforcement learning agents using general, task-independent concepts such as novelty and interest can learn multiple, task-oriented behaviors by adapting their focus of attention in response to their changing experiences in their environment.