Mastering Chess and Shogi by Self-Play with a …

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, 1Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, 1Matthew Lai, Arthur Guez, Marc Lanctot,1

Deep Residual Learning for Image Recognition - …

Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com

  Image, Learning, Residual, Recognition, Residual learning for image recognition

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …

  Intelligence, Collection

arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

For all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …

@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016

where 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …

A Tutorial on UAVs for Wireless Networks: …

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …

  Network, Communication, Wireless, Wireless communications, Wireless networks

Adversarial Generative Nets: Neural Network …

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University

  Network, Attacks, Nets, Adversarial generative nets, Adversarial, Generative, Neural network, Neural, Neural network attacks

Massive Exploration of Neural Machine Translation ...

Massive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain

  Architecture, Machine, Exploration, Translation, Neural, Exploration of neural machine translation, Exploration of neural machine translation architectures

Going deeper with convolutions - arXiv

Going deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet

  With, Going, Going deeper with convolutions, Deeper, Convolutions

Andrew G. Howard Menglong Zhu Bo Chen Dmitry ...

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam

  Applications

Abstract - arXiv

quence model can be applied to reinforcement learning problems without the need for the components usually associated with RL algorithms. 3 Reinforcement Learning and Control as Sequence Modeling In this section, we describe the training procedure for our sequence model and discuss how it can be used for control.

  Control, Learning, Reinforcement, Reinforcement learning, Reinforcement learning and control

Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further,

  Control, Learning, Reinforcement, Reinforcement learning

Dueling Network Architectures for Deep Reinforcement …

Over the past years, deep learning has contributed to dra-matic advances in scalability and performance of machine learning (LeCun et al., 2015). One exciting application is the sequential decision-making setting of reinforcement learning (RL) and control. Notable examples include deep Q-learning (Mnih et al., 2015), deep visuomotor policies

  Network, Control, Learning, And control, Reinforcement, Reinforcement learning

Benchmarking Safe Exploration in Deep Reinforcement …

range of prior work on safe reinforcement learning, we propose to standardize constrained RL as the main formalism for safe exploration. Second, we present the Safety Gym benchmark suite, a new slate of high-dimensional continuous control environments for measuring research progress on constrained RL. Finally, we

  Control, Learning, Reinforcement, Reinforcement learning

Deep Reinforcement Learning Nanodegree Program Syllabus

addition of reinforcement learning theory and programming techniques. This program will not prepare you for a specific career or role, rather, it will grow your deep learning and reinforcement learning expertise, and give you the skills you need to understand the most recent advancements in deep reinforcement learning,

  Learning, Reinforcement, Reinforcement learning

Asynchronous Methods for Deep Reinforcement Learning

3. Reinforcement Learning Background We consider the standard reinforcement learning setting where an agent interacts with an environment Eover a number of discrete time steps. At each time step t, the agent receives a state s tand selects an action a tfrom some set of possible actions Aaccording to its policy ˇ, where ˇis a mapping from states s

  Learning, Reinforcement, Asynchronous, Reinforcement learning

Lecture 1: Introduction to Reinforcement Learning

Lecture 1: Introduction to Reinforcement Learning The RL Problem Reward Rewards Areward R t is a scalar feedback signal Indicates how well agent is doing at step t The agent’s job is to maximise cumulative reward Reinforcement learning is based on thereward hypothesis De nition (Reward Hypothesis) All goals can be described by the ...

  Learning, Reinforcement, Reinforcement learning