Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games

Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games

Peng Peng (1), Quan Yuan (1), Ying Wen (2), Yaodong Yang (2), Zhenkun Tang (1), Haitao Long (1), Jun Wang (2)

(1) Alibaba Group, (2) University College London

(arXiv:1703.10069, Submitted on 29 Mar 2017)

Real-world artificial intelligence (AI) applications often require multiple agents to work in a collaborative effort

node2vec: Scalable Feature Learning for Networks

node2vec: Scalable Feature Learning for Networks

Aditya Grover, Jure Leskovec

Stanford University

adityag@cs.stanford.edu,jure@cs.stanford.edu

KDD ’16, August 13 - 17, 2016, San Francisco, CA, USA

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms.

Awesome Papers: 2017-02-4

ACTOR-MIMIC: DEEP MULTITASK AND TRANSFER REINFORCEMENT LEARNING

Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov

The ability to act in multiple environments and transfer previous knowledge to new

Awesome Papers: 2017-02-4

ACTOR-MIMIC: DEEP MULTITASK AND TRANSFER REINFORCEMENT LEARNING

Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov

The ability to act in multiple environments and transfer previous knowledge to new

Awesome Papers: 2017-02-1

On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox

Chandan Gautam, Aruna Tiwari and Qian Leng

One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines.

Awesome Papers: 2017-01-4

A K-fold Method for Baseline Estimation in Policy Gradient Algorithms

Nithyanand Kota, Abhishek Mishra, Sunil Srinivasa, Xi (Peter) Chen, Pieter Abbeel

The high variance issue in unbiased policy-gradient methods such as VPG and REINFORCE is typically mitigated by adding a baseline. However, the baseline fitting itself suffers from the underfitting or the overfitting problem. In this paper, we develop a K-fold method for baseline estimation in policy gradient algorithms.

Awesome Papers: 2017-01-3

Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes

Ludovic Hofer, Hugo Gimbert

This paper presents a new method to learn online policies in continuous state, continuous action, model-free Markov decision processes, with two properties that are crucial for practical applications.

Awesome Papers: 2017-01-2

Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review

Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao

The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning.

Awesome Papers: 2017-01-1

iCaRL: Incremental Classifier and Representation Learning

Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Christoph H. Lampert

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data.

Awesome Papers: 2016-12-4

Deep Reinforcement Learning with Averaged Target DQN

Oron Anschel, Nir Baram, Nahum Shimkin

The commonly used Q-learning algorithm combined with function approximation induces systematic overestimations of state-action values. These systematic errors might cause instability, poor performance and sometimes divergence of learning.

Awesome Papers: 2016-12-3

Deep Convolutional Neural Network Design Patterns

Leslie N. Smith, Nicholay Topin

Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications.

Awesome Papers: 2016-12-2

Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Jascha Sohl-Dickstein,Eric A Weiss,Niru Maheswaranathan, Surya Ganguli

A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable.

Awesome Papers: 2016-12-1

Intriguing properties of neural networks

Christian Szegedy, Wojciech Zaremba,Ilya Sutskever,Joan Bruna, Dumitru Erhan,Ian Goodfellow, Rob Fergus

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties.

Awesome Papers: 2016-12-2

Bayesian Optimization for Machine Learning : A Practical Guidebook

Ian Dewancker, Michael McCourt, Scott Clark

The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices.

Awesome Papers: 2016-12-1

Intriguing properties of neural networks

Christian Szegedy, Wojciech Zaremba,Ilya Sutskever,Joan Bruna, Dumitru Erhan,Ian Goodfellow, Rob Fergus

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties.

Awesome Papers: 2016-11-1

Towards Deep Symbolic Reinforcement Learning

Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets

Awesome Papers: 2016-09-1

Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server

Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Yee Whye Teh, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell

Awesome Papers: 2016-09-2

Awesome Paper 2016-06-1

Input: Query and All related documents process:

  1. Tokenize all documents and query into a vectors. input: all the query and documents

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