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    Deep Learning to Learn 

    Abbeel, Pieter (Georgia Institute of Technology, 2018-08-20)
    Reinforcement learning and imitation learning have seen success in many domains, including autonomous helicopter flight, Atari, simulated locomotion, Go, robotic manipulation. However, sample complexity of these methods ...
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    Learning Locomotion: From Simulation to Real World 

    Tan, Jie (2021-09-01)
    Deep Reinforcement Learning (DRL) holds the promise of designing complex robotic controllers automatically. In this talk, I will discuss two different approaches to apply deep reinforcement learning to learn locomotion ...
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    Compressed computation of good policies in large MDPs 

    Szepesvari, Csaba (2021-03-10)
    Markov decision processes (MDPs) is a minimalist framework to capture that many tasks require long-term plans and feedback due to noisy dynamics. Yet, as a result MDPs lack structure and as such planning and learning in ...
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    Global Optimality Guarantees for Policy Gradient Methods 

    Russo, Daniel (2020-03-11)
    Policy gradients methods are perhaps the most widely used class of reinforcement learning algorithms. These methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a ...
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    ML@GT Lab presents LAB LIGHTNING TALKS 2020 

    AlRegib, Ghassan; Chau, Duen Horng (Polo); Chava, Sudheer; Cohen, Morris; Davenport, Mark A.; Desai, Deven; Dovrolis, Constantine; Essa, Irfan A.; Gupta, Swati; Huo, Xiaoming; Kira, Zsolt; Li, Jing; Maguluri, Siva Theja; Pananjady, Ashwin; Prakash, B. Aditya; Riedl, Mark; Romberg, Justin K.; Xie, Yao; Zhang, Xiuwei (2020-12-04)
    Labs affiliated with the Machine Learning Center at Georgia Tech (ML@GT) will have the opportunity to share their research interests, work, and unique aspects of their lab in three minutes or less to interested graduate ...
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    Towards a Theory of Representation Learning for Reinforcement Learning 

    Agarwal, Alekh (2021-09-15)
    Provably sample-efficient reinforcement learning from rich observational inputs remains a key open challenge in research. While impressive recent advances have allowed the use of linear modelling while carrying out ...
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    The Statistical Foundations of Learning to Control 

    Recht, Benjamin (2018-11-14)
    Given the dramatic successes in machine learning and reinforcement learning over the past half decade, there has been a surge of interest in applying these techniques to continuous control problems in robotics and autonomous ...

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    AuthorAbbeel, Pieter (1)Agarwal, Alekh (1)Al-Regib, Ghassan (1)Chau, Duen Horng (Polo) (1)Chava, Sudheer (1)Cohen, Morris (1)Davenport, Mark A. (1)Desai, Deven (1)Dovrolis, Constantine (1)Essa, Irfan A. (1)... View MoreSubject
    Reinforcement learning (7)
    Robotics (3)Machine learning (2)Antenna (1)Artificial intelligence (AI) (1)Computational ophthalmology (1)Control (1)Data science (1)Deep learning (1)Healthcare (1)... View MoreDate Issued2021 (3)2018 (2)2020 (2)Has File(s)Yes (7)
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