- Sep 2020
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Ozaita, J., Baronchelli, A., & Sánchez, A. (2020). The emergence of segregation: From observable markers to group specific norms. ArXiv:2009.05354 [Physics, q-Bio]. http://arxiv.org/abs/2009.05354
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journals.sagepub.com journals.sagepub.com
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Ludwig, V. U., Brown, K. W., & Brewer, J. A. (2020). Self-Regulation Without Force: Can Awareness Leverage Reward to Drive Behavior Change? Perspectives on Psychological Science, 1745691620931460. https://doi.org/10.1177/1745691620931460
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- May 2020
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Radulescu, A., Holmes, K., & Niv, Y. (2020). On the convergent validity of risk sensitivity measures [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/qdhx4
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psyarxiv.com psyarxiv.com
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Hertz, U. (2020). Cognitive learning processes account for asymmetries in adaptations to new social norms [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7thku
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- Apr 2020
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Ting, C., Palminteri, S., Lebreton, M., & Engelmann, J. B. (2020, March 25). The elusive effects of incidental anxiety on reinforcement-learning. https://doi.org/10.31234/osf.io/7d4tc MLA
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- Mar 2019
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cjc.ict.ac.cn cjc.ict.ac.cn
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深度强化学习综述
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cjc.ict.ac.cn cjc.ict.ac.cn
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深度强化学习综述
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github.com github.com
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reinforcement-learning code and paper tutorials
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- Feb 2019
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gitee.com gitee.com
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We present MILABOT: a deep reinforcement learning chatbot developed by theMontreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prizecompetition. MILABOT is capable of conversing with humans on popular smalltalk topics through both speech and text. The system consists of an ensemble ofnatural language generation and retrieval models, including template-based models,bag-of-words models, sequence-to-sequence neural network and latent variableneural network models. By applying reinforcement learning to crowdsourced dataand real-world user interactions, the system has been trained to select an appropriateresponse from the models in its ensemble. The system has been evaluated throughA/B testing with real-world users, where it performed significantly better thanmany competing systems. Due to its machine learning architecture, the system islikely to improve with additional data
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