S
Sharad Chitlangia
Researcher at Birla Institute of Technology and Science
Publications - 11
Citations - 42
Sharad Chitlangia is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 2, co-authored 9 publications receiving 25 citations.
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Quantized Reinforcement Learning (QuaRL)
Srivatsan Krishnan,Sharad Chitlangia,Maximilian Lam,Zishen Wan,Aleksandra Faust,Vijay Janapa Reddi +5 more
TL;DR: This first comprehensive empirical study that quantifies the effects of quantization on various deep reinforcement learning policies with the intent to reduce their computational resource demands and demonstrates real-world applications ofquantization for reinforcement learning.
Journal ArticleDOI
Reinforcement learning and its connections with neuroscience and psychology.
TL;DR: The authors comprehensively review a large number of findings in both neuroscience and psychology that evidence reinforcement learning as a promising candidate for modeling learning and decision-making in the brain and construct a mapping between various classes of modern RL algorithms and specific findings from both neurophysiological and behavioral literature.
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Incorporating Domain Knowledge into Deep Neural Networks.
TL;DR: Two broad approaches to encode domain-knowledge–as logical and numerical constraints–are examined and techniques and results obtained in several subcategories under each of these approaches are described.
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How to Tell Deep Neural Networks What We Know.
TL;DR: In this article, the authors present a short survey of ways in which existing scientific knowledge is included when constructing models with neural networks and examine the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks.
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Psychological and Neural Evidence for Reinforcement Learning: A Survey.
TL;DR: A number of findings are reviewed that establish evidence of key elements of the RL problem and solution being represented in regions of the brain.