Reinforcement Learning 공개
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Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practic ...
 
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Roman Ring is a Research Engineer at DeepMind. Featured References Grandmaster level in StarCraft II using multi-agent reinforcement learning Vinyals et al, 2019 Replicating DeepMind StarCraft II Reinforcement Learning Benchmark with Actor-Critic Methods Roman Ring, 2018 Additional References Relational Deep Reinforcement Learning, Zambaldi et al 2…
 
Shimon Whiteson is a Professor of Computer Science at Oxford University, the head of WhiRL, the Whiteson Research Lab at Oxford, and Head of Research at Waymo UK. Featured References VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann…
 
Aravind Srinivas is a 3rd year PhD student at UC Berkeley advised by Prof. Abbeel. He co-created and co-taught a grad course on Deep Unsupervised Learning at Berkeley. Featured References Data-Efficient Image Recognition with Contrastive Predictive Coding Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Esla…
 
Taylor Killian is a Ph.D. student at the University of Toronto and the Vector Institute, and an Intern at Google Brain. Featured References Direct Policy Transfer with Hidden Parameter Markov Decision Processes Yao, Killian, Konidaris, Doshi-Velez Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes Killian, Daulto…
 
Nan Jiang is an Assistant Professor of Computer Science at University of Illinois. He was a Postdoc Microsoft Research, and did his PhD at University of Michigan under Professor Satinder Singh. Featured References Reinforcement Learning: Theory and Algorithms, Alekh Agarwal Nan Jiang Sham M. Kakade Model-based RL in Contextual Decision Processes: P…
 
Danijar Hafner is a PhD student at the University of Toronto, and a student researcher at Google Research, Brain Team and the Vector Institute. He holds a Masters of Research from University College London. Featured References A deep learning framework for neuroscience Blake A. Richards, Timothy P. Lillicrap , Philippe Beaudoin, Yoshua Bengio, Rafa…
 
Csaba Szepesvari is: Head of the Foundations Team at DeepMind Professor of Computer Science at the University of Alberta Canada CIFAR AI Chair Fellow at the Alberta Machine Intelligence Institute Co-Author of the book Bandit Algorithms along with Tor Lattimore, and author of the book Algorithms for Reinforcement Learning References Bandit based mon…
 
Ben Eysenbach is a PhD student in the Machine Learning Department at Carnegie Mellon University. He was a Resident at Google Brain, and studied math and computer science at MIT. He co-founded the ICML Exploration in Reinforcement Learning workshop. Featured References Diversity is All You Need: Learning Skills without a Reward Function Benjamin Eys…
 
Thank you to all the presenters that participated. I covered as many as I could given the time and crowds, if you were not included and wish to be, please email talkrl@pathwayi.com More details on the official NeurIPS Deep RL Workshop site. 0:23 Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcem…
 
Scott Fujimoto is a PhD student at McGill University and Mila. He is the author of TD3 as well as some of the recent developments in batch deep reinforcement learning. Featured References Addressing Function Approximation Error in Actor-Critic Methods Scott Fujimoto, Herke van Hoof, David Meger Off-Policy Deep Reinforcement Learning without Explora…
 
Dr. Jessica Hamrick is a Research Scientist at DeepMind. She holds a PhD in Psychology from UC Berkeley. Featured References Structured agents for physical construction Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick Analogues of mental simulation and imagination i…
 
Dr Pablo Samuel Castro is a Staff Research Software Engineer at Google Brain. He is the main author of the Dopamine RL framework. Featured References A Comparative Analysis of Expected and Distributional Reinforcement LearningClare Lyle, Pablo Samuel Castro, Marc G. Bellemare A Geometric Perspective on Optimal Representations for Reinforcement Lear…
 
Dr. Kamyar Azizzadenesheli is a post-doctorate scholar at Caltech. His research interest is mainly in the area of Machine Learning, from theory to practice, with the main focus in Reinforcement Learning. He will be joining Purdue University as an Assistant CS Professor in Fall 2020. Featured References Efficient Exploration through Bayesian Deep Q-…
 
Antonin Raffin is a researcher at the German Aerospace Center (DLR) in Munich, working in the Institute of Robotics and Mechatronics. His research is on using machine learning for controlling real robots (because simulation is not enough), with a particular interest for reinforcement learning. Ashley Hill is doing his thesis on improving control al…
 
Michael L Littman is a professor of Computer Science at Brown University. He was elected ACM Fellow in 2018 "For contributions to the design and analysis of sequential decision making algorithms in artificial intelligence". Featured References Convergent Actor Critic by Humans James MacGlashan, Michael L. Littman, David L. Roberts, Robert Tyler Lof…
 
Natasha Jaques is a PhD candidate at MIT working on affective and social intelligence. She has interned with DeepMind and Google Brain, and was an OpenAI Scholars mentor. Her paper “Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning” received an honourable mention for best paper at ICML 2019. Featured References So…
 
August 2, 2019 Transcript The idea with TalkRL Podcast is to hear from brilliant folks from across the world of Reinforcement Learning, both research and applications. As much as possible, I want to hear from them in their own language. I try to get to know as much as I can about their work before hand. And Im not here to convert anyone, I want to …
 
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