Computational Modeling of Emotion-Motivated Decisions for Continuous Control of Mobile Robots

State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

[1]  Hong Qiao,et al.  Realizing human-like manipulation with a musculoskeletal system and biologically inspired control scheme , 2019, Neurocomputing.

[2]  Muneyoshi Hyodo,et al.  Emotion and Autobiographical Memory , 2014 .

[3]  Jason J. Corso,et al.  A Critical Investigation of Deep Reinforcement Learning for Navigation , 2018, ArXiv.

[4]  Gernot R. Müller-Putz,et al.  Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets , 2019, IEEE Transactions on Cognitive and Developmental Systems.

[5]  Razvan Pascanu,et al.  Imagination-Augmented Agents for Deep Reinforcement Learning , 2017, NIPS.

[6]  S. Grossberg.  On the production and release of chemical transmitters and related topics in cellular control. , 1969, Journal of theoretical biology.

[7]  G. Rainer,et al.  Cognitive neuroscience: Neural mechanisms for detecting and remembering novel events , 2003, Nature Reviews Neuroscience.

[8]  Masashi Sugiyama,et al.  Guide Actor-Critic for Continuous Control , 2017, ICLR.

[9]  Joel Z. Leibo,et al.  Model-Free Episodic Control , 2016, ArXiv.

[10]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[11]  Sergey Levine,et al.  End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..

[12]  Peijie Yin,et al.  A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity , 2016, IEEE Transactions on Cognitive and Developmental Systems.

[13]  Minoru Asada,et al.  Modeling Development of Multimodal Emotion Perception Guided by Tactile Dominance and Perceptual Improvement , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[14]  Marc Pollefeys,et al.  Episodic Curiosity through Reachability , 2018, ICLR.

[15]  Emily S. Cross,et al.  The Perception of Emotion in Artificial Agents , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[16]  Elizabeth A. Phelps,et al.  How arousal modulates memory: Disentangling the effects of attention and retention , 2004, Cognitive, affective & behavioral neuroscience.

[17]  Fengfu Li,et al.  Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning , 2016, IEEE Transactions on Cybernetics.

[18]  L. Schwabe,et al.  Stress Prompts Habit Behavior in Humans , 2009, The Journal of Neuroscience.

[19]  Mehdi Khamassi,et al.  Toward Self-Aware Robots , 2018, Front. Robot. AI.

[20]  P. Dayan,et al.  Algorithms for survival: a comparative perspective on emotions , 2017, Nature Reviews Neuroscience.

[21]  Jennifer A. Hobin,et al.  Ventral hippocampal muscimol disrupts context‐specific fear memory retrieval after extinction in rats , 2006, Hippocampus.

[22]  Karolina M. Lempert,et al.  Emotion and decision making: multiple modulatory neural circuits. , 2014, Annual review of neuroscience.

[23]  Catholijn M. Jonker,et al.  Emotion in reinforcement learning agents and robots: a survey , 2017, Machine Learning.

[24]  Alexei A. Efros,et al.  Curiosity-Driven Exploration by Self-Supervised Prediction , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[25]  Sergey Levine,et al.  Trust Region Policy Optimization , 2015, ICML.

[26]  Sham M. Kakade,et al.  Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control , 2018, ICLR.

[27]  Thomas L. Griffiths,et al.  Shaping Model-Free Reinforcement-Learning with Model-Based Pseudorewards , 2018 .

[28]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[29]  Kate Saenko,et al.  Learning Multi-Level Hierarchies with Hindsight , 2017, ICLR.

[30]  Tom Schaul,et al.  FeUdal Networks for Hierarchical Reinforcement Learning , 2017, ICML.

[31]  Yuval Tassa,et al.  Control-limited differential dynamic programming , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[32]  E. Koechlin,et al.  Motivation and cognitive control in the human prefrontal cortex , 2009, Nature Neuroscience.

[33]  A. Arnsten.  Stress signalling pathways that impair prefrontal cortex structure and function , 2009, Nature Reviews Neuroscience.

[34]  Sergey Levine,et al.  Data-Efficient Hierarchical Reinforcement Learning , 2018, NeurIPS.

[35]  Carl E. Rasmussen,et al.  PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.

[36]  Daniel Bullock,et al.  Anatomy and computational modeling of networks underlying cognitive-emotional interaction , 2013, Front. Hum. Neurosci..

[37]  Neil Burgess,et al.  Human hippocampal processing of environmental novelty during spatial navigation , 2014, Hippocampus.

[38]  K. Scherer,et al.  Appraisal processes in emotion. , 2003 .

[39]  Stephen Maren,et al.  Hippocampal involvement in contextual modulation of fear extinction , 2007, Hippocampus.

[40]  S. Shankar Sastry,et al.  Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning , 2017, ArXiv.

[41]  Gultekin Gulsen,et al.  Amygdala-hippocampal dynamics during salient information processing , 2017, Nature Communications.

[42]  Charles Blundell,et al.  Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.

[43]  Ana Paiva,et al.  Learning by appraising: an emotion-based approach to intrinsic reward design , 2014, Adapt. Behav..

[44]  J. Gross,et al.  The Neural Bases of Emotion Regulation: Reappraisal and Suppression of Negative Emotion , 2008, Biological Psychiatry.

[45]  G. Quirk,et al.  Dissociable Roles of Prelimbic and Infralimbic Cortices, Ventral Hippocampus, and Basolateral Amygdala in the Expression and Extinction of Conditioned Fear , 2011, Neuropsychopharmacology.

[46]  Wei Wu,et al.  Brain-Inspired Motion Learning in Recurrent Neural Network With Emotion Modulation , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[47]  Murray Shanahan,et al.  Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[48]  D. Bavelier,et al.  Influence of reward motivation on human declarative memory , 2016, Neuroscience & Biobehavioral Reviews.

[49]  Guoshi Li.  Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear , 2017 .

[50]  Guangwen Yang,et al.  Episodic Memory Deep Q-Networks , 2018, IJCAI.

[51]  Judit Zsuga,et al.  The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept. , 2016, Behavioral neuroscience.

[52]  P. Fossati,et al.  Neural correlates of emotion processing: From emotional to social brain , 2012, European Neuropsychopharmacology.

[53]  Matthew L. Shapiro,et al.  Motivational states activate distinct hippocampal representations to guide goal-directed behaviors , 2009, Proceedings of the National Academy of Sciences.

[54]  Alice Y. Chiang,et al.  Working-memory capacity protects model-based learning from stress , 2013, Proceedings of the National Academy of Sciences.

[55]  R. Cabeza,et al.  Cognitive neuroscience of emotional memory , 2006, Nature Reviews Neuroscience.