Reinforcement learning for robots using neural networks
Reinforcement learning for robots using neural networks
Affective computing
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
A social reinforcement learning agent
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
MINERVA: A Tour-Guide Robot that Learns
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Learning behavior-selection by emotions and cognition in a multi-goal robot task
The Journal of Machine Learning Research
Affective Learning — A Manifesto
BT Technology Journal
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Human computing and machine understanding of human behavior: a survey
Proceedings of the 8th international conference on Multimodal interfaces
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Teachable characters: user studies, design principles, and learning performance
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
How people talk when teaching a robot
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Towards an Affective Aware Home
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
Mobile robot navigation modulated by artificial emotions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AffectButton: A method for reliable and valid affective self-report
International Journal of Human-Computer Studies
Social signal and user adaptation in reinforcement learning-based dialogue management
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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Computer models can be used to investigate the role of emotion in learning. Here we present EARL, our framework for the systematic study of the relation between emotion, adaptation and reinforcement learning (RL). EARL enables the study of, among other things, communicated affect as reinforcement to the robot; the focus of this chapter. In humans, emotions are crucial to learning. For example, a parent--observing a child--uses emotional expression to encourage or discourage specific behaviors. Emotional expression can therefore be a reinforcement signal to a child. We hypothesize that affective facial expressions facilitate robot learning, and compare a social setting with a non-social one to test this. The non-social setting consists of a simulated robot that learns to solve a typical RL task in a continuous grid-world environment. The social setting additionally consists of a human (parent) observing the simulated robot (child). The human's emotional expressions are analyzed in real time and converted to an additional reinforcement signal used by the robot; positive expressions result in reward, negative expressions in punishment. We quantitatively show that the "social robot" indeed learns to solve its task significantly faster than its "non-social sibling". We conclude that this presents strong evidence for the potential benefit of affective communication with humans in the reinforcement learning loop.