Q-Learning with Basic Emotions
Q-learning is a simple and powerful tool in solving dynamic problems where environments are unknown. It uses a balance of exploration and exploitation to find an optimal solution to the problem. In this paper, we propose using four basic emotions: joy, sadness, fear, and anger to influence a Qlearning agent. Simulations show that the proposed affective agent requires lesser number of steps to find the optimal path. We found when affective agent finds the optimal path, the ratio between exploration to exploitation gradually decreases, indicating lower total step count in the long run
Code (0)
등록된 구현이 없습니다.
Tasks
Q-LearningSimilar Papers 제목 키워드 기반
Survey and Perspective on Social Emotions in Robotics
This study reviews research on social emotions in robotics. In robotics, the study of emotions has been pursued for a long time, including the study of their recognition, expression, and computational modeling of the bas…
SurveyBasic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion m…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningGANmut: Learning Interpretable Conditional Space for Gamut of Emotions
Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expression…
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain
We annotate 35,000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (none, we…
BenchmarkingEmotion RecognitionSentiment AnalysisMulti-Task Transformer with uncertainty modelling for Face Based Affective Computing
Face based affective computing consists in detecting emotions from face images. It is useful to unlock better automatic comprehension of human behaviours and could pave the way toward improved human-machines interactions…