Types of RNN

1) Plain Tanh Recurrent Nerual Networks

2) Gated Recurrent Neural Networks (GRU)

3) Long Short-Term Memory (LSTM)

Tutorials

The Unreasonable Effectiveness of Recurrent Neural Networks

• blog: http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Understanding LSTM Networks

• blog: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
• blog(zh): http://www.jianshu.com/p/9dc9f41f0b29

A Beginner’s Guide to Recurrent Networks and LSTMs

http://deeplearning4j.org/lstm.html

A Deep Dive into Recurrent Neural Nets

http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/

Exploring LSTMs

http://blog.echen.me/2017/05/30/exploring-lstms/

A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach

• paper: http://minds.jacobs-university.de/sites/default/files/uploads/papers/ESNTutorialRev.pdf
• slides: http://deeplearning.cs.cmu.edu/notes/shaoweiwang.pdf

Long Short-Term Memory: Tutorial on LSTM Recurrent Networks

http://people.idsia.ch/~juergen/lstm/index.htm

LSTM implementation explained

http://apaszke.github.io/lstm-explained.html

Recurrent Neural Networks Tutorial

• Part 1(Introduction to RNNs): http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
• Part 2(Implementing a RNN using Python and Theano): http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-2-implementing-a-language-model-rnn-with-python-numpy-and-theano/
• Part 3(Understanding the Backpropagation Through Time (BPTT) algorithm): http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/
• Part 4(Implementing a GRU/LSTM RNN): http://www.wildml.com/2015/10/recurrent-neural-network-tutorial-part-4-implementing-a-grulstm-rnn-with-python-and-theano/

Recurrent Neural Networks in DL4J

http://deeplearning4j.org/usingrnns.html

Learning RNN Hierarchies

• github: https://github.com/pranv/lrh/blob/master/about.md

Element-Research Torch RNN Tutorial for recurrent neural nets : let’s predict time series with a laptop GPU

• blog: https://christopher5106.github.io/deep/learning/2016/07/14/element-research-torch-rnn-tutorial.html

RNNs in Tensorflow, a Practical Guide and Undocumented Features

• blog: http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

Learning about LSTMs using Torch

• blog: http://kbullaughey.github.io/lstm-play/
• github: https://github.com/kbullaughey/lstm-play

Build a Neural Network (LIVE)

• intro: LSTM
• youtube: https://www.youtube.com/watch?v=KvoZU-ItDiE
• mirror: https://pan.baidu.com/s/1i4KoumL
• github: https://github.com/llSourcell/build_a_neural_net_live

Deriving LSTM Gradient for Backpropagation

http://wiseodd.github.io/techblog/2016/08/12/lstm-backprop/

TensorFlow RNN Tutorial

https://svds.com/tensorflow-rnn-tutorial/

RNN Training Tips and Tricks

https://github.com/karpathy/char-rnn#tips-and-tricks

Tips for Training Recurrent Neural Networks

http://danijar.com/tips-for-training-recurrent-neural-networks/

A Tour of Recurrent Neural Network Algorithms for Deep Learning

http://machinelearningmastery.com/recurrent-neural-network-algorithms-for-deep-learning/

Fundamentals of Deep Learning – Introduction to Recurrent Neural Networks

https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/

Essentials of Deep Learning : Introduction to Long Short Term Memory

https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/

How to build a Recurrent Neural Network in TensorFlow

How to build a Recurrent Neural Network in TensorFlow (1/7)

https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767#.2vozogqf7

Using the RNN API in TensorFlow (2/7)

https://medium.com/@erikhallstrm/tensorflow-rnn-api-2bb31821b185#.h0ycrjuo3

Using the LSTM API in TensorFlow (3/7)

https://medium.com/@erikhallstrm/using-the-tensorflow-lstm-api-3-7-5f2b97ca6b73#.k7aciqaxn

Using the Multilayered LSTM API in TensorFlow (4/7)

https://medium.com/@erikhallstrm/using-the-tensorflow-multilayered-lstm-api-f6e7da7bbe40#.dj7dy92m5

Using the DynamicRNN API in TensorFlow (5/7)

https://medium.com/@erikhallstrm/using-the-dynamicrnn-api-in-tensorflow-7237aba7f7ea#.49qw259ks

Using the Dropout API in TensorFlow (6/7)

https://medium.com/@erikhallstrm/using-the-dropout-api-in-tensorflow-2b2e6561dfeb#.a7mc3o9aq

Unfolding RNNs

Unfolding RNNs: RNN : Concepts and Architectures

• blog: http://suriyadeepan.github.io/2017-01-07-unfolding-rnn/

Unfolding RNNs II: Vanilla, GRU, LSTM RNNs from scratch in Tensorflow

• blog: http://suriyadeepan.github.io/2017-02-13-unfolding-rnn-2/
• github: https://github.com/suriyadeepan/rnn-from-scratch

Train RNN

On the difficulty of training Recurrent Neural Networks

• author: Razvan Pascanu, Tomas Mikolov, Yoshua Bengio
• arxiv: http://arxiv.org/abs/1211.5063
• video talks: http://techtalks.tv/talks/on-the-difficulty-of-training-recurrent-neural-networks/58134/

A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

• arxiv: http://arxiv.org/abs/1504.00941
• gitxiv: http://gitxiv.com/posts/7j5JXvP3kn5Jf8Waj/irnn-experiment-with-pixel-by-pixel-sequential-mnist
• github: https://github.com/fchollet/keras/blob/master/examples/mnist_irnn.py
• github: https://gist.github.com/GabrielPereyra/353499f2e6e407883b32
• blog(“Implementing Recurrent Neural Net using chainer!”): http://t-satoshi.blogspot.jp/2015/06/implementing-recurrent-neural-net-using.html
• reddit: https://www.reddit.com/r/MachineLearning/comments/31rinf/150400941_a_simple_way_to_initialize_recurrent/
• reddit: https://www.reddit.com/r/MachineLearning/comments/32tgvw/has_anyone_been_able_to_reproduce_the_results_in/

Batch Normalized Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1510.01378

Sequence Level Training with Recurrent Neural Networks

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06732
• github: https://github.com/facebookresearch/MIXER
• notes: https://www.evernote.com/shard/s189/sh/ada01a82-70a9-48d4-985c-20492ab91e84/8da92be19e704996dc2b929473abed46

Training Recurrent Neural Networks (PhD thesis)

• atuhor: Ilya Sutskever
• thesis: https://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf

Deep learning for control using augmented Hessian-free optimization

• blog: https://studywolf.wordpress.com/2016/04/04/deep-learning-for-control-using-augmented-hessian-free-optimization/
• github: https://github.com/studywolf/blog/blob/master/train_AHF/train_hf.py

Hierarchical Conflict Propagation: Sequence Learning in a Recurrent Deep Neural Network

• arxiv: http://arxiv.org/abs/1602.08118

Recurrent Batch Normalization

• arxiv: http://arxiv.org/abs/1603.09025
• github: https://github.com/iassael/torch-bnlstm
• github: https://github.com/cooijmanstim/recurrent-batch-normalization
• github(“LSTM with Batch Normalization”): https://github.com/fchollet/keras/pull/2183
• github: https://github.com/jihunchoi/recurrent-batch-normalization-pytorch
• notes: http://www.shortscience.org/paper?bibtexKey=journals/corr/CooijmansBLC16

Batch normalized LSTM for Tensorflow

• blog: http://olavnymoen.com/2016/07/07/rnn-batch-normalization
• github: https://github.com/OlavHN/bnlstm

Optimizing Performance of Recurrent Neural Networks on GPUs

• arxiv: http://arxiv.org/abs/1604.01946
• github: https://github.com/parallel-forall/code-samples/blob/master/posts/rnn/LSTM.cu

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

• arxiv: http://arxiv.org/abs/1605.07154

Explaining and illustrating orthogonal initialization for recurrent neural networks

• blog: http://smerity.com/articles/2016/orthogonal_init.html

Professor Forcing: A New Algorithm for Training Recurrent Networks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1610.09038
• github: https://github.com/anirudh9119/LM_GANS

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

• intro: Selected for an oral presentation at NIPS, 2016. University of Zurich and ETH Zurich
• arxiv: https://arxiv.org/abs/1610.09513
• github: https://github.com/dannyneil/public_plstm
• github: https://github.com/Enny1991/PLSTM
• github: https://github.com/philipperemy/tensorflow-phased-lstm
• github: https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/PhasedLSTMCell
• reddit: https://www.reddit.com/r/MachineLearning/comments/5bmfw5/r_phased_lstm_accelerating_recurrent_network/

Tuning Recurrent Neural Networks with Reinforcement Learning (RL Tuner)

• paper: http://openreview.net/pdf?id=BJ8fyHceg
• blog: https://magenta.tensorflow.org/2016/11/09/tuning-recurrent-networks-with-reinforcement-learning/
• github: https://github.com/tensorflow/magenta/tree/master/magenta/models/rl_tuner

Capacity and Trainability in Recurrent Neural Networks

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1611.09913

Large-Batch Training for LSTM and Beyond

• intro: UC Berkeley & UCLA & Google
• paper: https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-138.pdf

Learn To Execute Programs

Learning to Execute

• arxiv: http://arxiv.org/abs/1410.4615
• github: https://github.com/wojciechz/learning_to_execute
• github(Tensorflow): https://github.com/raindeer/seq2seq_experiments

Neural Programmer-Interpreters

• intro: Google DeepMind. ICLR 2016 Best Paper
• arxiv: http://arxiv.org/abs/1511.06279
• project page: http://www-personal.umic (Google DeepMind. ICLR 2016 Best Paper)h.edu/~reedscot/iclr_project.html
• github: https://github.com/mokemokechicken/keras_npi

A Programmer-Interpreter Neural Network Architecture for Prefrontal Cognitive Control

• paper: https://www.researchgate.net/publication/273912337_A_ProgrammerInterpreter_Neural_Network_Architecture_for_Prefrontal_Cognitive_Control

Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

• arxiv: http://arxiv.org/abs/1602.05875

Neural Random-Access Machines

• arxiv: http://arxiv.org/abs/1511.06392

Attention Models

Recurrent Models of Visual Attention

• intro: Google DeepMind. NIPS 2014
• arxiv: http://arxiv.org/abs/1406.6247
• data: https://github.com/deepmind/mnist-cluttered
• github: https://github.com/Element-Research/rnn/blob/master/examples/recurrent-visual-attention.lua

Recurrent Model of Visual Attention

• intro: Google DeepMind
• paper: http://arxiv.org/abs/1406.6247
• gitxiv: http://gitxiv.com/posts/ZEobCXSh23DE8a8mo/recurrent-models-of-visual-attention
• blog: http://torch.ch/blog/2015/09/21/rmva.html
• github: https://github.com/Element-Research/rnn/blob/master/scripts/evaluate-rva.lua

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

• arxiv: http://arxiv.org/abs/1502.03044
• github: https://github.com/kelvinxu/arctic-captions

A Neural Attention Model for Abstractive Sentence Summarization

• intro: EMNLP 2015. Facebook AI Research
• arxiv: http://arxiv.org/abs/1509.00685
• github: https://github.com/facebook/NAMAS

Effective Approaches to Attention-based Neural Machine Translation

• intro: EMNLP 2015
• paper: http://nlp.stanford.edu/pubs/emnlp15_attn.pdf
• project: http://nlp.stanford.edu/projects/nmt/
• github: https://github.com/lmthang/nmt.matlab

Generating Images from Captions with Attention

• arxiv: http://arxiv.org/abs/1511.02793
• github: https://github.com/emansim/text2image
• demo: http://www.cs.toronto.edu/~emansim/cap2im.html

Attention and Memory in Deep Learning and NLP

• blog: http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/

Survey on the attention based RNN model and its applications in computer vision

• arxiv: http://arxiv.org/abs/1601.06823

Attention in Long Short-Term Memory Recurrent Neural Networks

• blog: http://machinelearningmastery.com/attention-long-short-term-memory-recurrent-neural-networks/

How to Visualize Your Recurrent Neural Network with Attention in Keras

• blog: https://medium.com/datalogue/attention-in-keras-1892773a4f22
• github: https://github.com/datalogue/keras-attention

Papers

Generating Sequences With Recurrent Neural Networks

• arxiv: http://arxiv.org/abs/1308.0850
• github: https://github.com/hardmaru/write-rnn-tensorflow
• github: https://github.com/szcom/rnnlib
• blog: http://blog.otoro.net/2015/12/12/handwriting-generation-demo-in-tensorflow/

A Clockwork RNN

• arxiv: https://arxiv.org/abs/1402.3511
• github: https://github.com/makistsantekidis/clockworkrnn
• github: https://github.com/zergylord/ClockworkRNN

Unsupervised Learning of Video Representations using LSTMs

• intro: ICML 2015
• project page: http://www.cs.toronto.edu/~nitish/unsupervised_video/
• arxiv: http://arxiv.org/abs/1502.04681
• code: http://www.cs.toronto.edu/~nitish/unsupervised_video/unsup_video_lstm.tar.gz
• github: https://github.com/emansim/unsupervised-videos

An Empirical Exploration of Recurrent Network Architectures

• paper: http://jmlr.org/proceedings/papers/v37/jozefowicz15.pdf

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

• intro: ACL 2015. Tree RNNs aka Recursive Neural Networks
• arxiv: https://arxiv.org/abs/1503.00075
• slides: http://lit.eecs.umich.edu/wp-content/uploads/2015/10/tree-lstms.pptx
• gitxiv: http://www.gitxiv.com/posts/esrArT2iLmSfNRrto/tree-structured-long-short-term-memory-networks
• github: https://github.com/stanfordnlp/treelstm
• github: https://github.com/ofirnachum/tree_rnn

LSTM: A Search Space Odyssey

• arxiv: http://arxiv.org/abs/1503.04069
• notes: https://www.evernote.com/shard/s189/sh/48da42c5-8106-4f0d-b835-c203466bfac4/50d7a3c9a961aefd937fae3eebc6f540
• blog(“Dissecting the LSTM”): https://medium.com/jim-fleming/implementing-lstm-a-search-space-odyssey-7d50c3bacf93#.crg8pztop
• github: https://github.com/jimfleming/lstm_search

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

• arxiv: http://arxiv.org/abs/1503.01007
• github: https://github.com/facebook/Stack-RNN

A Critical Review of Recurrent Neural Networks for Sequence Learning

• arxiv: http://arxiv.org/abs/1506.00019
• review: http://blog.terminal.com/a-thorough-and-readable-review-on-rnns/

Visualizing and Understanding Recurrent Networks

• intro: ICLR 2016. Andrej Karpathy, Justin Johnson, Fei-Fei Li
• arxiv: http://arxiv.org/abs/1506.02078
• slides: http://www.robots.ox.ac.uk/~seminars/seminars/Extra/2015_07_06_AndrejKarpathy.pdf
• github: https://github.com/karpathy/char-rnn

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

• intro: Winner of MSCOCO image captioning challenge, 2015
• arxiv: http://arxiv.org/abs/1506.03099

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

• arxiv: https://arxiv.org/abs/1506.04214
• github: https://github.com/loliverhennigh/Convolutional-LSTM-in-Tensorflow

Grid Long Short-Term Memory

• arxiv: http://arxiv.org/abs/1507.01526
• github(Torch7): https://github.com/coreylynch/grid-lstm/

Depth-Gated LSTM

• arxiv: http://arxiv.org/abs/1508.03790
• github: GitHub(dglstm.h+dglstm.cc)

Deep Knowledge Tracing

• paper: https://web.stanford.edu/~cpiech/bio/papers/deepKnowledgeTracing.pdf
• github: https://github.com/chrispiech/DeepKnowledgeTracing

Top-down Tree Long Short-Term Memory Networks

• arxiv: http://arxiv.org/abs/1511.00060
• github: https://github.com/XingxingZhang/td-treelstm

Improving performance of recurrent neural network with relu nonlinearity

• intro: ICLR 2016
• arxiv: https://arxiv.org/abs/1511.03771

Alternative structures for character-level RNNs

• intro: INRIA & Facebook AI Research. ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06303
• github: https://github.com/facebook/Conditional-character-based-RNN

Long Short-Term Memory-Networks for Machine Reading

• arxiv: http://arxiv.org/abs/1601.06733
• github: https://github.com/cheng6076/SNLI-attention

Lipreading with Long Short-Term Memory

• arxiv: http://arxiv.org/abs/1601.08188

Associative Long Short-Term Memory

• arxiv: http://arxiv.org/abs/1602.03032
• github: https://github.com/mohammadpz/Associative_LSTM

Representation of linguistic form and function in recurrent neural networks

• arxiv: http://arxiv.org/abs/1602.08952

Architectural Complexity Measures of Recurrent Neural Networks

• arxiv: http://arxiv.org/abs/1602.08210

Easy-First Dependency Parsing with Hierarchical Tree LSTMs

• arxiv: http://arxiv.org/abs/1603.00375

Training Input-Output Recurrent Neural Networks through Spectral Methods

• arxiv: http://arxiv.org/abs/1603.00954

Sequential Neural Models with Stochastic Layers

• arxiv: https://arxiv.org/abs/1605.07571
• github: https://github.com/marcofraccaro/srnn

Neural networks with differentiable structure

• arxiv: http://arxiv.org/abs/1606.06216
• github: https://github.com/ThomasMiconi/DiffRNN

What You Get Is What You See: A Visual Markup Decompiler

• project page: http://lstm.seas.harvard.edu/latex/
• arxiv: http://arxiv.org/abs/1609.04938
• github: https://github.com/harvardnlp/im2markup
• github(Tensorflow): https://github.com/ssampang/im2latex
• github: https://github.com/opennmt/im2text
• github: https://github.com/ritheshkumar95/im2latex-tensorflow

Hybrid computing using a neural network with dynamic external memory

• intro: Nature 2016
• keywords: Differentiable Neural Computer (DNC) https://www.nature.com/articles/nature20101.epdf?author_access_token=ImTXBI8aWbYxYQ51Plys8NRgN0jAjWel9jnR3ZoTv0MggmpDmwljGswxVdeocYSurJ3hxupzWuRNeGvvXnoO8o4jTJcnAyhGuZzXJ1GEaD-Z7E6X_a9R-xqJ9TfJWBqz
• github: https://github.com/deepmind/dnc

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks

• project page: https://imatge-upc.github.io/skiprnn-2017-telecombcn/
• arxiv: https://arxiv.org/abs/1708.06834

Dilated Recurrent Neural Networks

• intro: NIPS 2017. IBM & University of Illinois at Urbana-Champaign
• keywords: DilatedRNN
• arxiv: https://arxiv.org/abs/1710.02224
• github(Tensorflow): https://github.com/code-terminator/DilatedRNN https://github.com/zalandoresearch/pt-dilate-rnn

Excitation Backprop for RNNs

https://arxiv.org/abs/1711.06778

Recurrent Relational Networks for Complex Relational Reasoning

• project page: https://rasmusbergpalm.github.io/recurrent-relational-networks/
• arxiv: https://arxiv.org/abs/1711.08028
• github: https://github.com//rasmusbergpalm/recurrent-relational-networks

Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition

• intro: University of Electronic Science and Technology of China & Brown University & University of Utah & XJERA LABS PTE.LTD
• arxiv: https://arxiv.org/abs/1712.05134

LSTMVis

Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

• homepage: http://lstm.seas.harvard.edu/
• demo: http://lstm.seas.harvard.edu/client/index.html
• arxiv: https://arxiv.org/abs/1606.07461
• github: https://github.com/HendrikStrobelt/LSTMVis

Recurrent Memory Array Structures

• arxiv: https://arxiv.org/abs/1607.03085
• github: https://github.com/krocki/ArrayLSTM

Recurrent Highway Networks

• author: Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber
• arxiv: http://arxiv.org/abs/1607.03474
• github(Tensorflow+Torch): https://github.com/julian121266/RecurrentHighwayNetworks/

DeepSoft: A vision for a deep model of software

• arxiv: http://arxiv.org/abs/1608.00092

Recurrent Neural Networks With Limited Numerical Precision

• arxiv: http://arxiv.org/abs/1608.06902

Hierarchical Multiscale Recurrent Neural Networks

• arxiv: http://arxiv.org/abs/1609.01704
• notes: https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/hm-rnn.md
• notes: https://medium.com/@jimfleming/notes-on-hierarchical-multiscale-recurrent-neural-networks-7362532f3b64#.pag4kund0

LightRNN

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1610.09893

Full-Capacity Unitary Recurrent Neural Networks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1611.00035
• github: https://github.com/stwisdom/urnn

DeepCoder: Learning to Write Programs

• arxiv: https://arxiv.org/abs/1611.01989

shuttleNet: A biologically-inspired RNN with loop connection and parameter sharing

• arxiv: https://arxiv.org/abs/1611.05216

Tracking the World State with Recurrent Entity Networks

• intro: Facebook AI Research
• arxiv: https://arxiv.org/abs/1612.03969
• github(Official): https://github.com/facebook/MemNN/tree/master/EntNet-babi

Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

• intro: National University of Singapore & Peking University
• arxiv: https://arxiv.org/abs/1612.08534

Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1701.03441
• github: https://github.com/jingweimo/Modified-LSTM

The Statistical Recurrent Unit

• intro: CMU
• arxiv: https://arxiv.org/abs/1703.00381

Factorization tricks for LSTM networks

• intro: ICLR 2017 Workshop
• arxiv: https://arxiv.org/abs/1703.10722
• github: https://github.com/okuchaiev/f-lm

Bayesian Recurrent Neural Networks

• intro: UC Berkeley
• arxiv: https://arxiv.org/abs/1704.02798
• github: https://github.com/mirceamironenco/BayesianRecurrentNN

Fast-Slow Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1705.08639
• github: https://github.com/amujika/Fast-Slow-LSTM

Visualizing LSTM decisions

https://arxiv.org/abs/1705.08153

Recurrent Additive Networks

• intro: [University of Washington & Allen Institute for Artificial Intelligence
• arxiv: https://arxiv.org/abs/1705.07393
• paper: http://www.kentonl.com/pub/llz.2017.pdf
• github(PyTorch): https://github.com/bheinzerling/ran

Recent Advances in Recurrent Neural Networks

• intro: University of Toronto & University of Waterloo
• arxiv: https://arxiv.org/abs/1801.01078

Grow and Prune Compact, Fast, and Accurate LSTMs

https://arxiv.org/abs/1805.11797

Projects

NeuralTalk (Deprecated): a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences

• github: https://github.com/karpathy/neuraltalk

NeuralTalk2: Efficient Image Captioning code in Torch, runs on GPU

• github: https://github.com/karpathy/neuraltalk2

char-rnn in Blocks

• github: https://github.com/johnarevalo/blocks-char-rnn

Project: pycaffe-recurrent

• code: https://github.com/kuprel/pycaffe-recurrent/

Using neural networks for password cracking

• blog: https://0day.work/using-neural-networks-for-password-cracking/
• github: https://github.com/gehaxelt/RNN-Passwords

torch-rnn: Efficient, reusable RNNs and LSTMs for torch

• github: https://github.com/jcjohnson/torch-rnn

Deploying a model trained with GPU in Torch into JavaScript, for everyone to use

• blog: http://testuggine.ninja/blog/torch-conversion
• demo: http://testuggine.ninja/DRUMPF-9000/
• github: https://github.com/Darktex/char-rnn

LSTM implementation on Caffe

• github: https://github.com/junhyukoh/caffe-lstm

JNN: Java Neural Network Library

• intro: C2W model, LSTM-based Language Model, LSTM-based Part-Of-Speech-Tagger Model
• github: https://github.com/wlin12/JNN

LSTM-Autoencoder: Seq2Seq LSTM Autoencoder

• github: https://github.com/cheng6076/LSTM-Autoencoder

RNN Language Model Variations

• intro: Standard LSTM, Gated Feedback LSTM, 1D-Grid LSTM
• github: https://github.com/cheng6076/mlm

keras-extra: Extra Layers for Keras to connect CNN with RNN

• github: https://github.com/anayebi/keras-extra

Dynamic Vanilla RNN, GRU, LSTM,2layer Stacked LSTM with Tensorflow Higher Order Ops

• github: https://github.com/KnHuq/Dynamic_RNN_Tensorflow

PRNN: A fast implementation of recurrent neural network layers in CUDA

• intro: Baidu Research
• blog: https://svail.github.io/persistent_rnns/
• github: https://github.com/baidu-research/persistent-rnn

min-char-rnn: Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy

• github: https://github.com/weixsong/min-char-rnn

rnn: Recurrent Neural Network library for Torch7’s nn

• github: https://github.com/Element-Research/rnn

word-rnn-tensorflow: Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow

• github: https://github.com/hunkim/word-rnn-tensorflow

tf-char-rnn: Tensorflow implementation of char-rnn

• github: https://github.com/shagunsodhani/tf-char-rnn

translit-rnn: Automatic transliteration with LSTM

• blog: http://yerevann.github.io/2016/09/09/automatic-transliteration-with-lstm/
• github: https://github.com/YerevaNN/translit-rnn

tf_lstm.py: Simple implementation of LSTM in Tensorflow in 50 lines (+ 130 lines of data generation and comments)

• gist: https://gist.github.com/nivwusquorum/b18ce332bde37e156034e5d3f60f8a23

Handwriting generating with RNN

• github: https://github.com/Arn-O/kadenze-deep-creative-apps/blob/master/final-project/glyphs-rnn.ipynb

RecNet - Recurrent Neural Network Framework

• github: https://github.com/joergfranke/recnet

Blogs

Survey on Attention-based Models Applied in NLP

http://yanran.li/peppypapers/2015/10/07/survey-attention-model-1.html

Survey on Advanced Attention-based Models

http://yanran.li/peppypapers/2015/10/07/survey-attention-model-2.html

Online Representation Learning in Recurrent Neural Language Models

http://www.marekrei.com/blog/online-representation-learning-in-recurrent-neural-language-models/

Fun with Recurrent Neural Nets: One More Dive into CNTK and TensorFlow

http://esciencegroup.com/2016/03/04/fun-with-recurrent-neural-nets-one-more-dive-into-cntk-and-tensorflow/

Materials to understand LSTM

https://medium.com/@shiyan/materials-to-understand-lstm-34387d6454c1#.4mt3bzoau

Understanding LSTM and its diagrams

:star::star::star::star::star:

• blog: https://medium.com/@shiyan/understanding-lstm-and-its-diagrams-37e2f46f1714
• slides: https://github.com/shi-yan/FreeWill/blob/master/Docs/Diagrams/lstm_diagram.pptx

Persistent RNNs: 30 times faster RNN layers at small mini-batch sizes

Persistent RNNs: Stashing Recurrent Weights On-Chip

• intro: Greg Diamos, Baidu Silicon Valley AI Lab
• paper: http://jmlr.org/proceedings/papers/v48/diamos16.pdf
• blog: http://svail.github.io/persistent_rnns/
• slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6673-greg-diamos-persisten-rnns.pdf

All of Recurrent Neural Networks

https://medium.com/@jianqiangma/all-about-recurrent-neural-networks-9e5ae2936f6e#.q4s02elqg

Rolling and Unrolling RNNs

https://shapeofdata.wordpress.com/2016/04/27/rolling-and-unrolling-rnns/

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow: LSTM regression using TensorFlow

• blog: http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html
• github: https://github.com/mouradmourafiq/tensorflow-lstm-regression

LSTMs

• blog: https://shapeofdata.wordpress.com/2016/06/04/lstms/

Machines and Magic: Teaching Computers to Write Harry Potter

• blog: https://medium.com/@joycex99/machines-and-magic-teaching-computers-to-write-harry-potter-37839954f252#.4fxemal9t
• github: https://github.com/joycex99/hp-word-model

Crash Course in Recurrent Neural Networks for Deep Learning

http://machinelearningmastery.com/crash-course-recurrent-neural-networks-deep-learning/

Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/

Recurrent Neural Networks in Tensorflow

• part I: http://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html
• part II: http://r2rt.com/recurrent-neural-networks-in-tensorflow-ii.html

Written Memories: Understanding, Deriving and Extending the LSTM

http://r2rt.com/written-memories-understanding-deriving-and-extending-the-lstm.html

Attention and Augmented Recurrent Neural Networks

• blog: http://distill.pub/2016/augmented-rnns/
• github: https://github.com/distillpub/post–augmented-rnns

Interpreting and Visualizing Neural Networks for Text Processing

https://civisanalytics.com/blog/data-science/2016/09/22/neural-network-visualization/

A simple design pattern for recurrent deep learning in TensorFlow

• blog: https://medium.com/@devnag/a-simple-design-pattern-for-recurrent-deep-learning-in-tensorflow-37aba4e2fd6b#.homq9zsyr
• github: https://github.com/devnag/tensorflow-bptt

RNN Spelling Correction: To crack a nut with a sledgehammer

• blog: https://medium.com/@yaoyaowd/rnn-spelling-correction-to-crack-a-nut-with-a-sledgehammer-7f5aa442c08c#.mc2ycyfda

Recurrent Neural Network Gradients, and Lessons Learned Therein

• blog: http://willwolf.io/en/2016/10/13/recurrent-neural-network-gradients-and-lessons-learned-therein/

A noob’s guide to implementing RNN-LSTM using Tensorflow

http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/

Non-Zero Initial States for Recurrent Neural Networks

• blog: http://r2rt.com/non-zero-initial-states-for-recurrent-neural-networks.html

Interpreting neurons in an LSTM network

http://yerevann.github.io/2017/06/27/interpreting-neurons-in-an-LSTM-network/

Optimizing RNN (Baidu Silicon Valley AI Lab)

Optimizing RNN performance

• blog: http://svail.github.io/rnn_perf/

Optimizing RNNs with Differentiable Graphs

• blog: http://svail.github.io/diff_graphs/
• notes: http://research.baidu.com/svail-tech-notes-optimizing-rnns-differentiable-graphs/

Resources

Awesome Recurrent Neural Networks - A curated list of resources dedicated to RNN

• homepage: http://jiwonkim.org/awesome-rnn/
• github: https://github.com/kjw0612/awesome-rnn

Jürgen Schmidhuber’s page on Recurrent Neural Networks

http://people.idsia.ch/~juergen/rnn.html

Reading and Questions

Are there any Recurrent convolutional neural network network implementations out there ?

• reddit: https://www.reddit.com/r/MachineLearning/comments/4chu3y/are_there_any_recurrent_convolutional_neural/

Tutorials

Demystifying Deep Reinforcement Learning (Part1)

http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/

Deep Reinforcement Learning With Neon (Part2)

http://neuro.cs.ut.ee/deep-reinforcement-learning-with-neon/

Deep Reinforcement Learning

• intro: David Silver, Google DeepMind
• slides: http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-iclr2015.pdf
• mirror: http://pan.baidu.com/s/1qWBOJGo

Deep Reinforcement Learning

• intro: MLSS 2016. John Schulman[UC Berkeley]
• homepage: http://rl-gym-doc.s3-website-us-west-2.amazonaws.com/mlss/index.html
• slides: http://pan.baidu.com/s/1jIatusA#path=%252F

Deep Reinforcement Learning: Pong from Pixels

• intro: Andrej Karpathy
• blog: http://karpathy.github.io/2016/05/31/rl/
• gist: https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5

Deep Reinforcement Learning

• instructor: David Silver. RLDM 2015
• video: http://videolectures.net/rldm2015_silver_reinforcement_learning/

Deep Reinforcement Learning

• intro: David Silver [Google DeepMind]
• video: http://techtalks.tv/talks/deep-reinforcement-learning/62360/
• slides: http://hunch.net/~beygel/deep_rl_tutorial.pdf

The Nuts and Bolts of Deep RL Research

• intro: NIPS 2016, John Schulman, OpenAI
• slides: http://rll.berkeley.edu/deeprlcourse/docs/nuts-and-bolts.pdf
• mirror: https://pan.baidu.com/s/1kVkBLkF

ML Tutorial: Modern Reinforcement Learning and Video Games

• intro: by Marc Bellemare [DeepMind]
• youtube: https://www.youtube.com/watch?v=WuFMrk3ZbkE
• mirror: https://www.bilibili.com/video/av17360035/

Reinforcement learning explained

• blog: https://www.oreilly.com/ideas/reinforcement-learning-explained

Beginner’s guide to Reinforcement Learning & its implementation in Python

https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/

Reinforcement Learning on the Web

• intro: Andrej Karpathy
• slides: https://docs.google.com/presentation/d/1lcYrN56V2_SuX1rSmpzOUeMnheF6Jsu33-MsvLW9O_4/edit#slide=id.p
• slides: http://alpha.openai.com/ak_rework_2017.pdf

Deep Q Learning with Keras and Gym

• blog: https://keon.io/rl/deep-q-learning-with-keras-and-gym/
• github: https://github.com/keon/deep-q-learning

“Deep Reinforcement Learning, Decision Making, and Control

• intro: ICML 2017 Tutorial
• slides: https://sites.google.com/view/icml17deeprl

A Tour of Reinforcement Learning: The View from Continuous Control

• intro: by Benjamin Recht, UC Berkeley
• slides: https://people.eecs.berkeley.edu/~brecht/l2c-icml2018/Recht_ICML_Control-RL_tutorial.pdf

An Introduction to Deep Reinforcement Learning

• intro: McGill University & Google Brain
• arxiv: https://arxiv.org/abs/1811.12560

Simple Reinforcement Learning with Tensorflow

Part 0: Q-Learning with Tables and Neural Networks https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.oo105wa2t

Part 1 - Two-armed Bandit

https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149#.tk89k51ob

Part 2 - Policy-based Agents

https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724#.n2wytg9q0

Part 3 - Model-Based RL https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99#.742i2yj6p

Part 4: Deep Q-Networks and Beyond https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df#.jox069crz

Part 5: Visualizing an Agent’s Thoughts and Actions https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-5-visualizing-an-agents-thoughts-and-actions-4f27b134bb2a#.pluh6cygm

Part 6: Partial Observability and Deep Recurrent Q-Networks

• blog: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-6-partial-observability-and-deep-recurrent-q-68463e9aeefc#.3se46qkzy
• github: https://gist.github.com/awjuliani/35d2ab3409fc818011b6519f0f1629df

Part 7: Action-Selection Strategies for Exploration

• blog: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-7-action-selection-strategies-for-exploration-d3a97b7cceaf#.8mcaa5nbe
• demo: https://awjuliani.github.io/exploration/index.html

Dissecting Reinforcement Learning

• part 1: https://mpatacchiola.github.io/blog/2016/12/09/dissecting-reinforcement-learning.html
• part 2: https://mpatacchiola.github.io/blog/2017/01/15/dissecting-reinforcement-learning-2.html
• part 3: https://mpatacchiola.github.io/blog/2017/01/29/dissecting-reinforcement-learning-3.html
• github: https://github.com/mpatacchiola/dissecting-reinforcement-learning

REINFORCE tutorial

• intro: A small collection of code snippets and notes explaining the foundations of the REINFORCE algorithm.
• github: https://github.com/mathias-madsen/reinforce_tutorial

Deep Q-Learning Recap

http://blog.davidqiu.com/Research/%5B%20Recap%20%5D%20Deep%20Q-Learning%20Recap/

Introduction to Reinforcement Learning

• intro: Joelle Pineau [McGill University]
• video: http://videolectures.net/deeplearning2016_pineau_reinforcement_learning/
• slides: http://videolectures.net/site/normal_dl/tag=1051677/deeplearning2016_pineau_reinforcement_learning_01.pdf

Courses

Advanced Topics: RL

UCL Course on RL

• instructors: David Silver (Google DeepMind, AlphaGo)
• homepage: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
• youtube: https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
• video: http://pan.baidu.com/s/1bnWGuIz/
• assignment: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/Easy21-Johannes.pdf

CS 294: Deep Reinforcement Learning, Fall 2017

• instructor: Sergey Levine
• homepage: http://rll.berkeley.edu/deeprlcourse/
• youtube: https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3
• bilibili: https://www.bilibili.com/video/av21501169/

CS 294: Deep Reinforcement Learning, Spring 2017

• course page: http://rll.berkeley.edu/deeprlcoursesp17/
• github: https://github.com//txizzle/drl

Berkeley CS 294: Deep Reinforcement Learning

• instructors: John Schulman, Pieter Abbeel
• homepage: http://rll.berkeley.edu/deeprlcourse/
• youtube: https://www.youtube.com/playlist?list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX
• mirror: https://pan.baidu.com/s/1hsQcm1Y

(Udacity) Reinforcement Learning - Offered at Georgia Tech as CS 8803

• instructor: Charles Isbell, Michael Littman
• homepage: https://www.udacity.com/course/reinforcement-learning–ud600
• homepage: https://classroom.udacity.com/courses/ud820/lessons/684808907/concepts/6512308530923

CS229 Lecture notes Part XIII: Reinforcement Learning and Control

• intro: Andrew Ng
• lecture notes: http://cs229.stanford.edu/notes/cs229-notes12.pdf

Practical_RL: A course in reinforcement learning in the wild

• github: https://github.com/yandexdataschool/Practical_RL

Reinforcement Learning (COMP-762) Winter 2017

• course page: http://www.cs.mcgill.ca/~dprecup/courses/rl.html
• lectures: http://www.cs.mcgill.ca/~dprecup/courses/RL/lectures.html

• lectures: https://sites.google.com/view/deep-rl-bootcamp/lectures
• video: https://www.bilibili.com/video/av15568836/

CMPUT 366: Intelligent Systems and CMPUT 609: Reinforcement Learning & Artificial Intelligence

• intro: by Rich Sutton, Adam White
• lecture video: https://drive.google.com/drive/folders/0B3w765rOKuKAMG9lbmRacFdsLWM?direction=a

Deep Reinforcement Learning and Control (Spring 2017, CMU 10703)

• instructors: Katerina Fragkiadaki, Ruslan Satakhutdinov
• homepage: https://katefvision.github.io/
• video: https://www.youtube.com/playlist?list=PLpIxOj-HnDsNPFdu2UqCu2McJKHs-eWXv
• mirror: https://www.bilibili.com/video/av18865689/

Advanced Deep Learning & Reinforcement Learning

• intro: DeepMind
• youtube: https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs
• bilibili: https://www.bilibili.com/video/av36621866/
• github: https://github.com/RylanSchaeffer/ucl-adv-dl-rl

Papers

Playing Atari with Deep Reinforcement Learning

• intro: Google DeepMind. NIPS Deep Learning Workshop 2013
• arxiv: http://arxiv.org/abs/1312.5602
• github: https://github.com/kristjankorjus/Replicating-DeepMind
• demo: http://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html
• github: https://github.com/Kaixhin/Atari
• github(Tensorflow): https://github.com/gliese581gg/DQN_tensorflow
• summary: https://github.com/aleju/papers/blob/master/neural-nets/Playing_Atari_with_Deep_Reinforcement_Learning.md

Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

• intro: NIPS 2014
• keywords: DQN, MCTS
• paper: http://papers.nips.cc/paper/5421-scalable-inference-for-neuronal-connectivity-from-calcium-imaging
• paper: https://web.eecs.umich.edu/~baveja/Papers/UCTtoCNNsAtariGames-FinalVersion.pdf

Replicating the Paper “Playing Atari with Deep Reinforcement Learning”

• intro: University of Tartu
• technical report: https://courses.cs.ut.ee/MTAT.03.291/2014_spring/uploads/Main/Replicating%20DeepMind.pdf

A Tutorial for Reinforcement Learning

• paper: http://web.mst.edu/~gosavia/tutorial.pdf
• code(C): http://web.mst.edu/~gosavia/bookcodes.html
• code(Matlab): http://web.mst.edu/~gosavia/mrrl_website.html

Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

• arxiv: http://arxiv.org/abs/1507.00814
• notes: https://www.evernote.com/shard/s189/sh/a4262b84-a322-4f77-9a76-569278be84af/b8c3e146a76ca3853f560bb03b60a481

Massively Parallel Methods for Deep Reinforcement Learning

• intro: ICML 2015. DeepMind
• keywords: DQN, Gorila
• arxiv: https://arxiv.org/abs/1507.04296

Action-Conditional Video Prediction using Deep Networks in Atari Games

• homepage: https://sites.google.com/a/umich.edu/junhyuk-oh/action-conditional-video-prediction
• arxiv: http://arxiv.org/abs/1507.08750
• github: https://github.com/junhyukoh/nips2015-action-conditional-video-prediction
• video: http://video.weibo.com/show?fid=1034:98062f3d83e41da6faa99cde5aa1ac97

Deep Recurrent Q-Learning for Partially Observable MDPs

• intro: AAAI 2015
• arxiv: https://arxiv.org/abs/1507.06527

Continuous control with deep reinforcement learning

• intro: Google DeepMind
• arxiv: http://arxiv.org/abs/1509.02971
• github: https://github.com/iassael/torch-policy-gradient
• github: https://github.com/stevenpjg/ddpg-aigym
• github(TensorFlow + OpenAI Gym): https://github.com/SimonRamstedt/ddpg

Benchmarking for Bayesian Reinforcement Learning

• arxiv: http://arxiv.org/abs/1509.04064
• code: https://github.com/mcastron/BBRL/
• reading: http://blogs.ulg.ac.be/damien-ernst/benchmarking-for-bayesian-reinforcement-learning/

Deep Reinforcement Learning with Double Q-learning

• intro: AAAI 2016
• arxiv: https://arxiv.org/abs/1509.06461

Giraffe: Using Deep Reinforcement Learning to Play Chess

• arxiv: http://arxiv.org/abs/1509.01549

Human-level control through deep reinforcement learning

• intro: Google DeepMind. 2015 Nature
• paper: http://www.readcube.com/articles/10.1038/nature14236?shared_access_token=Lo_2hFdW4MuqEcF3CVBZm9RgN0jAjWel9jnR3ZoTv0P5kedCCNjz3FJ2FhQCgXkApOr3ZSsJAldp-tw3IWgTseRnLpAc9xQq-vTA2Z5Ji9lg16_WvCy4SaOgpK5XXA6ecqo8d8J7l4EJsdjwai53GqKt-7JuioG0r3iV67MQIro74l6IxvmcVNKBgOwiMGi8U0izJStLpmQp6Vmi_8Lw_A%3D%3D
• paper: http://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf
• github(Lua/Torch): https://github.com/deepmind/dqn
• mirror: http://pan.baidu.com/s/1kTiwzOF
• code: https://sites.google.com/a/deepmind.com/dqn/
• youtube: https://www.youtube.com/watch?v=V2wzkPmiB_A
• github: https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner
• github: https://github.com/tambetm/simple_dqn
• github: https://github.com/devsisters/DQN-tensorflow
• reddit: https://www.reddit.com/r/MachineLearning/comments/2x4yy1/google_deepmind_nature_paper_humanlevel_control

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

• arxiv: http://arxiv.org/abs/1510.02173

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

• intro: Google DeepMind
• arxiv: http://arxiv.org/abs/1509.08731
• notes: https://www.evernote.com/shard/s189/sh/8c7ff9d9-c321-4e83-a802-58f55ebed9ac/bfc614113180a5f4624390df56e73889

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06342
• github: https://github.com/eparisotto/ActorMimic

MazeBase: A Sandbox for Learning from Games

• intro: New York University & Facebook AI Research
• arxiv: http://arxiv.org/abs/1511.07401

Learning Simple Algorithms from Examples

• intro: New York University & Facebook AI Research
• arxiv: http://arxiv.org/abs/1511.07275
• github: https://github.com/wojzaremba/algorithm-learning

Learning Algorithms from Data

• PhD thesis: http://www.cs.nyu.edu/media/publications/zaremba_wojciech.pdf
• github: https://github.com/wojzaremba/algorithm-learning

Multiagent Cooperation and Competition with Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1511.08779
• github: https://github.com/NeuroCSUT/DeepMind-Atari-Deep-Q-Learner-2Player

Active Object Localization with Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1511.06015

Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions

• arxiv: http://arxiv.org/abs/1512.01124

How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies

• arxiv: http://arxiv.org/abs/1512.02011

State of the Art Control of Atari Games Using Shallow Reinforcement Learning

• arxiv: http://arxiv.org/abs/1512.01563

Angrier Birds: Bayesian reinforcement learning

• arxiv: http://arxiv.org/abs/1601.01297
• github: https://github.com/imanolarrieta/angrybirds
• gitxiv: http://gitxiv.com/posts/Nr2N7j4YrR4gnCYK9/angrier-birds-bayesian-reinforcement-learning

Prioritized Experience Replay

• arxiv: http://arxiv.org/abs/1511.05952

Dueling Network Architectures for Deep Reinforcement Learning

• intro: ICML 2016 best paper
• arxiv: http://arxiv.org/abs/1511.06581
• notes: https://hadovanhasselt.wordpress.com/2016/06/20/best-paper-at-icml-dueling-network-architectures-for-deep-reinforcement-learning/

Asynchronous Methods for Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1602.01783
• github(Tensorflow): https://github.com/traai/async-deep-rl
• github(Tensorflow+Keras+OpenAI Gym): https://github.com/coreylynch/async-rl
• github(Tensorflow): https://github.com/devsisters/async-rl-tensorflow
• github(PyTorch): https://github.com/ikostrikov/pytorch-a3c
• notes: https://blog.acolyer.org/2016/10/10/asynchronous-methods-for-deep-reinforcement-learning/

Graying the black box: Understanding DQNs

• arxiv: http://arxiv.org/abs/1602.02658

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

• arxiv: http://arxiv.org/abs/1602.02672

Value Iteration Networks

• intro: NIPS 2016, Best Paper Award. University of California, Berkeley
• arxiv: http://arxiv.org/abs/1602.02867
• github(official, Theano): https://github.com/avivt/VIN
• github: https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks
• github: https://github.com/onlytailei/PyTorch-value-iteration-networks
• github: https://github.com/kentsommer/pytorch-value-iteration-networks
• github: https://github.com/neka-nat/vin-keras
• notes(by Andrej Karpathy): https://github.com/karpathy/paper-notes/blob/master/vin.md

Insights in Reinforcement Learning

• intro: MSc thesis
• mirror: http://pan.baidu.com/s/1bn51BYJ

Using Deep Q-Learning to Control Optimization Hyperparameters

• arxiv: http://arxiv.org/abs/1602.04062

Continuous Deep Q-Learning with Model-based Acceleration

• arxiv: http://arxiv.org/abs/1603.00748

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

• arxiv: http://arxiv.org/abs/1603.01121

Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

• intro: MIT
• arxiv: https://arxiv.org/abs/1604.06057
• github: https://github.com/EthanMacdonald/h-DQN

Benchmarking Deep Reinforcement Learning for Continuous Control

• arxiv: http://arxiv.org/abs/1604.06778
• github: https://github.com/rllab/rllab
• doc: https://rllab.readthedocs.org/en/latest/

Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning

• homepage: http://www.cs.ubc.ca/~van/papers/2016-TOG-deepRL/index.html
• paper: http://www.cs.ubc.ca/~van/papers/2016-TOG-deepRL/2016-TOG-deepRL.pdf
• github: https://github.com/xbpeng/DeepTerrainRL

Hierarchical Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks

• arxiv: http://arxiv.org/abs/1605.05359

Deep Successor Reinforcement Learning (MIT)

• arxiv: http://arxiv.org/abs/1606.02396
• github: https://github.com/Ardavans/DSR

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

• arxiv: https://arxiv.org/abs/1605.06676
• github: https://github.com/iassael/learning-to-communicate

Deep Reinforcement Learning with Regularized Convolutional Neural Fitted Q Iteration RC-NFQ: Regularized Convolutional Neural Fitted Q Iteration

• intro: A batch algorithm for deep reinforcement learning. Incorporates dropout regularization and convolutional neural networks with a separate target Q network.
• paper: http://machineintelligence.org/papers/rc-nfq.pdf
• github: https://github.com/cosmoharrigan/rc-nfq

Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks

• intro: Facebook AI Research
• arxiv: http://arxiv.org/abs/1609.02993

Bayesian Reinforcement Learning: A Survey

• arxiv: http://arxiv.org/abs/1609.04436

Playing FPS Games with Deep Reinforcement Learning

• arxiv: http://arxiv.org/abs/1609.05521
• demo: https://www.youtube.com/playlist?list=PLduGZax9wmiHg-XPFSgqGg8PEAV51q1FT
• notes: https://blog.acolyer.org/2016/11/23/playing-fps-games-with-deep-reinforcement-learning/

Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States

• intro: University of Washington & UC Berkeley
• arxiv: https://arxiv.org/abs/1610.01112

Utilization of Deep Reinforcement Learning for saccadic-based object visual search

• arxiv: https://arxiv.org/abs/1610.06492

Learning to Navigate in Complex Environments

• intro: Google DeepMind
• arxiv: https://arxiv.org/abs/1611.03673
• github: https://github.com/deepmind/lab
• youtube: https://www.youtube.com/watch?v=lNoaTyMZsWI

Reinforcement Learning with Unsupervised Auxiliary Tasks

• intro: DeepMind. ICLR 2017 oral
• arxiv: https://arxiv.org/abs/1611.05397

Learning to reinforcement learn

• intro: DeepMind
• arxiv: https://arxiv.org/abs/1611.05763

A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games

• intro: Graduate Training Center of Neuroscience & MSR
• arxiv: https://arxiv.org/abs/1611.07078

Exploration for Multi-task Reinforcement Learning with Deep Generative Models

• intro: NIPS Deep Reinforcement Learning Workshop 2016
• arxiv: https://arxiv.org/abs/1611.09894

Neural Combinatorial Optimization with Reinforcement Learning

• intro: Google Brain
• keywords: traveling salesman problem (TSP)
• arxiv: https://arxiv.org/abs/1611.09940

Loss is its own Reward: Self-Supervision for Reinforcement Learning

• arxiv: https://arxiv.org/abs/1612.07307

Reinforcement Learning Using Quantum Boltzmann Machines

• intro: 1QB Information Technologies (1QBit)
• arxiv: https://arxiv.org/abs/1612.05695

Deep Reinforcement Learning applied to the game Bubble Shooter

• bachelor thesis: https://staff.fnwi.uva.nl/b.bredeweg/pdf/BSc/20152016/Samson.pdf
• github: https://github.com/laurenssam/AlphaBubble
• demo: https://www.youtube.com/watch?v=DPAKFenNgbs

Deep Reinforcement Learning: An Overview

• arxiv: https://arxiv.org/abs/1701.07274

Robust Adversarial Reinforcement Learning

• intro: CMU & Google Brain & Google Research
• arxiv: https://arxiv.org/abs/1703.02702

Beating Atari with Natural Language Guided Reinforcement Learning

• intro: Stanford University
• arxiv: https://arxiv.org/abs/1704.05539

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

• intro: Imperial College London
• arxiv: https://arxiv.org/abs/1705.06769
• github: https://github.com/Nat-D/FeatureControlHRL

Distral: Robust Multitask Reinforcement Learning

• intro: DeepMind
• keywords: Distill, transfer learning
• arxiv: https://arxiv.org/abs/1707.04175

Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations

• intro: Syracuse University & University of California, Riverside
• arxiv: https://arxiv.org/abs/1710.03792

Robust Deep Reinforcement Learning with Adversarial Attacks

https://arxiv.org/abs/1712.03632

Variational Deep Q Network

• intro: Second workshop on Bayesian Deep Learning (NIPS 2017). Columbia University
• arxiv: https://arxiv.org/abs/1711.11225

On Monte Carlo Tree Search and Reinforcement Learning

https://www.jair.org/media/5507/live-5507-10333-jair.pdf

Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes

• intro: deepsense.ai & Intel & Polish Academy of Sciences
• arxiv: https://arxiv.org/abs/1801.02852
• gihtub: https://github.com//anonymous-author1/DDRL

GAN Q-learning

https://arxiv.org/abs/1805.04874

Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents

• intro: Visual Geometry Group, University of Oxford & Element AI & Polytechnique Montreal, Mila & Canada CIFAR AI Chair
• arxiv: https://arxiv.org/abs/1904.01318

Surveys

Reinforcement Learning: A Survey

• intro: JAIR 1996
• project page: http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/rl-survey.html
• arxiv: http://arxiv.org/abs/cs/9605103

A Brief Survey of Deep Reinforcement Learning

• intro: IEEE Signal Processing Magazine, Special Issue on Deep Learning for Image Understanding
• intro: Imperial College London & Arizona State University
• arxiv: https://arxiv.org/abs/1708.05866

Playing Doom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

• arxiv: http://arxiv.org/abs/1605.02097
• github: https://github.com/Marqt/ViZDoom
• homepage: http://vizdoom.cs.put.edu.pl/
• tutorial: http://vizdoom.cs.put.edu.pl/tutorial

Deep Reinforcement Learning From Raw Pixels in Doom

• intro: Bachelor’s thesis
• arxiv: https://arxiv.org/abs/1610.02164

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

• intro: University of Oxford
• arxiv: https://arxiv.org/abs/1612.00380

Reinforcement Learning via Recurrent Convolutional Neural Networks

• intro: ICPR 2016
• arxiv: https://arxiv.org/abs/1701.02392
• github: https://github.com/tanmayshankar/RCNN_MDP

Shallow Updates for Deep Reinforcement Learning

• intro: The Technion & UC Berkeley
• arxiv: https://arxiv.org/abs/1705.07461
• github(Official): https://github.com/Shallow-Updates-for-Deep-RL/Shallow_Updates_for_Deep_RL

Projects

TorchQLearning

• github: https://github.com/SeanNaren/TorchQLearningExample

General_Deep_Q_RL: General deep Q learning framework

• github: https://github.com/VinF/General_Deep_Q_RL
• wiki: https://github.com/VinF/General_Deep_Q_RL/wiki

Snake: Toy example of deep reinforcement model playing the game of snake

• github: https://github.com/bitwise-ben/Snake

Using Deep Q Networks to Learn Video Game Strategies

• github: https://github.com/asrivat1/DeepLearningVideoGames

qlearning4k: Q-learning for Keras

• intro: “Qlearning4k is a reinforcement learning add-on for the python deep learning library Keras. Its simple, and is ideal for rapid prototyping.”
• github: https://github.com/farizrahman4u/qlearning4k

rlenvs: Reinforcement learning environments for Torch7, inspired by RL-Glue

• github: https://github.com/Kaixhin/rlenvs

deep_rl_ale: An implementation of Deep Reinforcement Learning / Deep Q-Networks for Atari games in TensorFlow

• github: https://github.com/Jabberwockyll/deep_rl_ale

Chimp: General purpose framework for deep reinforcement learning

• github: https://github.com/sisl/Chimp

Deep Q Learning for ATARI using Tensorflow

• github: https://github.com/mrkulk/deepQN_tensorflow

DeepQLearning: A powerful machine learning algorithm utilizing Q-Learning and Neural Networks, implemented using Torch and Lua.

• github: https://github.com/blakeMilner/DeepQLearning

OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms

• homepage: https://gym.openai.com/
• github: https://github.com/openai/gym

DeeR: DEEp Reinforcement learning framework

• github: https://github.com/VinF/deer/
• docs: http://deer.readthedocs.io/en/latest/

KeRLym: A Deep Reinforcement Learning Toolbox in Keras

• homepage: https://oshearesearch.com/index.php/2016/06/14/kerlym-a-deep-reinforcement-learning-toolbox-in-keras/
• github: https://github.com/osh/kerlym

Pack of Drones: Layered reinforcement learning for complex behaviors

• github: https://github.com/MickyDowns/deep-theano-rnn-lstm-car
• youtube: https://www.youtube.com/watch?v=WrLRGzbfeZc

RL Helicopter Game: Q-Learning and DQN Reinforcement Learning to play the Helicopter Game - Keras based!

• project page: http://dandxy89.github.io/rf_helicopter/
• github: https://github.com/dandxy89/rf_helicopter

Playing Mario with Deep Reinforcement Learning

• github: https://github.com/aleju/mario-ai

Deep Attention Recurrent Q-Network

• intro: Deep Reinforcement Learning Workshop, NIPS 2015. DeepHack Game
• arxiv: https://arxiv.org/abs/1512.01693
• github: https://github.com/5vision/DARQN

Deep Reinforcement Learning in TensorFlow

• intro: TensorFlow implementation of Deep Reinforcement Learning papers
• github: https://github.com/carpedm20/deep-rl-tensorflow

rltorch: A RL package for Torch that can also be used with openai gym

• github: https://github.com/ludc/rltorch

deep_q_rl: Theano-based implementation of Deep Q-learning

• github: https://github.com/spragunr/deep_q_rl

Reinforcement-trading

• intro: This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.
• github: https://github.com/deependersingla/deep_trader

dist-dqn：Distributed Reinforcement Learning using Deep Q-Network in TensorFlow

• github: https://github.com/viswanathgs/dist-dqn

Deep Reinforcement Learning for Keras

• github: https://github.com/matthiasplappert/keras-rl

RL4J: Reinforcement Learning for the JVM

• intro: Reinforcement learning framework integrated with deeplearning4j.
• github: https://github.com/deeplearning4j/rl4j

Teaching Your Computer To Play Super Mario Bros. – A Fork of the Google DeepMind Atari Machine Learning Project

• blog: http://www.ehrenbrav.com/2016/08/teaching-your-computer-to-play-super-mario-bros-a-fork-of-the-google-deepmind-atari-machine-learning-project/
• github: https://github.com/ehrenbrav/DeepQNetwork

dprl: Deep reinforcement learning package for torch7

• github: https://github.com/PoHsunSu/dprl

Reinforcement Learning for Torch: Introducing torch-twrl

• blog: https://blog.twitter.com/2016/reinforcement-learning-for-torch-introducing-torch-twrl
• github: https://github.com/twitter/torch-twrl

Alpha Toe - Using Deep learning to master Tic-Tac-Toe - Daniel Slater

• blog: http://www.danielslater.net/2016/10/alphatoe.html
• youtube: https://www.youtube.com/watch?v=Meb5hApAnj4
• github: https://github.com/DanielSlater/AlphaToe

Tensorflow-Reinforce: Implementation of Reinforcement Learning Models in Tensorflow

• github: https://github.com/yukezhu/tensorflow-reinforce

deep RL hacking on minecraft with malmo

• github: https://github.com/matpalm/malmomo

ReinforcementLearning

• intro: MC control, Q-learning, SARSA, Cross Entropy Method
• github: https://github.com/janivanecky/ReinforcementLearning

markovjs: Reinforcement Learning in JavaScript

• github: https://github.com/lsunsi/markovjs

Deep Q: Deep reinforcement learning with TensorFlow

• github: https://github.com/tobegit3hub/deep_q

Deep Q-Learning Network in pytorch

https://github.com/transedward/pytorch-dqn

Tensorflow-RL: Implementations of deep RL papers and random experimentation

https://github.com/steveKapturowski/tensorflow-rl

Minimal and Clean Reinforcement Learning Examples

https://github.com/rlcode/reinforcement-learning

DeepRL: Highly modularized implementation of popular deep RL algorithms by PyTorch

https://github.com/ShangtongZhang/DeepRL

Autonomous vehicle navigation

Self-Driving-Car-AI

• intro: A simple self-driving car AI python script using the deep Q-learning algorithm
• github: https://github.com//JianyangZhang/Self-Driving-Car-AI

Autonomous vehicle navigation based on Deep Reinforcement Learning

https://github.com//kaihuchen/DRL-AutonomousVehicles

Car Racing using Reinforcement Learning

• intro: Stanford University
• paper: https://web.stanford.edu/class/cs221/2017/restricted/p-final/elibol/final.pdf

Play Flappy Bird

Using Deep Q-Network to Learn How To Play Flappy Bird

• github: https://github.com/yenchenlin/DeepLearningFlappyBird

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow)

• blog: http://blog.csdn.net/songrotek/article/details/50951537
• github: https://github.com/songrotek/DRL-FlappyBird

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN)

• github: https://github.com/li-haoran/DRL-FlappyBird

MXNET-Scala Playing Flappy Bird Using Deep Reinforcement Learning

• github: https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/DRLFlappyBird

Flappy Bird Bot using Reinforcement Learning in Python

• github: https://github.com/chncyhn/flappybird-qlearning-bot

Using Keras and Deep Q-Network to Play FlappyBird

• blog: https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html
• github: https://github.com/yanpanlau/Keras-FlappyBird

Pong

Building a Pong playing AI in just 1 hour(plus 4 days training…)

• sildes: https://speakerdeck.com/danielslater/building-a-pong-ai
• github: https://github.com/DanielSlater/PyDataLondon2016
• youtube: https://www.youtube.com/watch?v=n8NdT_3y9oY

Pong Neural Network(LIVE)

• youtube: https://www.youtube.com/watch?v=Hqf__FlRlzg
• github: https://github.com/llSourcell/pong_neural_network_live

Tips and Tricks

DeepRLHacks

• intro: The Nuts and Bolts of Deep RL Research
• github: https://github.com/williamFalcon/DeepRLHacks

Library

BURLAP: Brown-UMBC Reinforcement Learning and Planning (BURLAP) java code library

• intro: for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them
• homepage: http://burlap.cs.brown.edu/

AgentNet: Deep Reinforcement Learning library for humans

• intro: A lightweight library to build and train deep reinforcement learning and custom recurrent networks using Theano+Lasagne
• github: https://github.com/yandexdataschool/AgentNet

Atari Multitask & Transfer Learning Benchmark (AMTLB)

• intro: Atari gauntlet for RL agents
• project page: http://ai-on.org/projects/multitask-and-transfer-learning.html
• github: https://github.com/deontologician/atari_multitask

Coach: a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms

• intro: Reinforcement Learning Coach by Intel® Nervana™ enables easy experimentation with state of the art Reinforcement Learning algorithms
• homepage: http://coach.nervanasys.com/
• github: https://github.com/NervanaSystems/coach

Blogs

Reinforcement learning’s foundational flaw

https://thegradient.pub/why-rl-is-flawed/

A Short Introduction To Some Reinforcement Learning Algorithms

http://webdocs.cs.ualberta.ca/~vanhasse/rl_algs/rl_algs.html

A Painless Q-Learning Tutorial

http://mnemstudio.org/path-finding-q-learning-tutorial.htm

Reinforcement Learning - Part 1

http://outlace.com/Reinforcement-Learning-Part-1/

Reinforcement Learning - Monte Carlo Methods

http://outlace.com/Reinforcement-Learning-Part-2/

Q-learning with Neural Networks

http://outlace.com/Reinforcement-Learning-Part-3/

Guest Post (Part I): Demystifying Deep Reinforcement Learning

http://www.nervanasys.com/demystifying-deep-reinforcement-learning/

Using reinforcement learning in Python to teach a virtual car to avoid obstacles: An experiment in Q-learning, neural networks and Pygame.

• blog: https://medium.com/@harvitronix/using-reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-6e782cc7d4c6#.p8ug6snri
• github: https://github.com/harvitronix/reinforcement-learning-car

Reinforcement learning in Python to teach a virtual car to avoid obstacles — part 2

https://medium.com/@harvitronix/reinforcement-learning-in-python-to-teach-a-virtual-car-to-avoid-obstacles-part-2-93e614fcd238#.i0o643m1h

Some Reinforcement Learning Algorithms in Python, C++

• pan: http://pan.baidu.com/s/1mhcYf3M#path=%252FImplementations%2520of%2520Some%2520Reinforcement%2520Learning%2520Algorithms

learning to do laps with reinforcement learning and neural nets

• blog: http://matpalm.com/blog/drivebot/
• github: https://github.com/matpalm/drivebot

Get a taste of reinforcement learning — implement a tic tac toe agent

https://medium.com/@shiyan/get-a-taste-of-reinforcement-learning-implement-a-tic-tac-toe-agent-deda5617b2e4#.59bx71a2h

Best reinforcement learning libraries?

• reddit: https://www.reddit.com/r/MachineLearning/comments/4b2ugc/best_reinforcement_learning_libraries/

Super Simple Reinforcement Learning Tutorial

• part 1: https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149
• part 2: https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724#.dyhxww1u6
• part 3: https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-3-model-based-rl-9a6fe0cce99#.r4c7i7tjq
• gist: https://gist.github.com/awjuliani/16608e1c4968baaa692b9b8c7dd94d04

Reinforcement Learning in Python

• github: https://github.com/NathanEpstein/pydata-reinforce

The Skynet Salesman

• keyworkds: traveling salesman problem (TSP), deep Q learning
• blog: http://multithreaded.stitchfix.com/blog/2016/07/21/skynet-salesman/
• github: https://github.com/jn2clark/ReinforcementLearning/tree/master/DeepQ

Apprenticeship learning using Inverse Reinforcement Learning

• blog: https://jangirrishabh.github.io/2016/07/09/virtual-car-IRL/
• github: https://github.com/jangirrishabh/toyCarIRL

Reinforcement Learning and DQN, learning to play from pixels

• blog: https://rubenfiszel.github.io/posts/rl4j/2016-08-24-Reinforcement-Learning-and-DQN.html

Deep Learning in a Nutshell: Reinforcement Learning

https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-reinforcement-learning/

Write an AI to win at Pong from scratch with Reinforcement Learning

https://medium.com/@dhruvp/how-to-write-a-neural-network-to-play-pong-from-scratch-956b57d4f6e0#.n1pgn9chr

Learning Reinforcement Learning (with Code, Exercises and Solutions)

• blog: http://www.wildml.com/2016/10/learning-reinforcement-learning/
• github: https://github.com/dennybritz/reinforcement-learning

Deep Reinforcement Learning: Playing a Racing Game

https://lopespm.github.io/machine_learning/2016/10/06/deep-reinforcement-learning-racing-game.html

Experimenting with Reinforcement Learning and Active Inference

• blog: http://www.araya.org/archives/955
• github: https://github.com/arayabrain/BinarySearchLSTM

Deep reinforcement learning, battleship

• blog: http://efavdb.com/battleship/
• github: https://github.com/EFavDB/battleship

Deep Learning Research Review Week 2: Reinforcement Learning

https://adeshpande3.github.io/adeshpande3.github.io/Deep-Learning-Research-Review-Week-2-Reinforcement-Learning

Reinforcement Learning: Artificial Intelligence in Game Playing

https://medium.com/@pavelkordik/reinforcement-learning-the-hardest-part-of-machine-learning-b667a22995ca#.jjiitflok

Artificial Intelligence’s Next Big Step: Reinforcement Learning

http://thenewstack.io/reinforcement-learning-ready-real-world/

Let’s make a DQN

Let’s make a DQN

• Theory: https://jaromiru.com/2016/09/27/lets-make-a-dqn-theory/
• Implementation: https://jaromiru.com/2016/10/03/lets-make-a-dqn-implementation/
• Debugging: https://jaromiru.com/2016/10/12/lets-make-a-dqn-debugging/
• Full DQN: https://jaromiru.com/2016/10/21/lets-make-a-dqn-full-dqn/
• github: https://github.com/jaara/AI-blog/blob/master/CartPole-basic.py

Books

Reinforcement Learning: State-of-the-Art

• intro: “The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.”
• book: http://www.springer.com/gp/book/9783642276446#

Reinforcement Learning: An Introduction

• github: https://github.com/Mononofu/reinforcement-learning
• homepage: http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
• course: http://incompleteideas.net/rlai.cs.ualberta.ca/RLAI/RLAIcourse/2010.html
• book(1st edition): http://pan.baidu.com/s/1jkaMq
• book(2rd edition): http://pan.baidu.com/s/1dDnNEnR

Reinforcement Learning: An Introduction (Second edition, Draft)

• book: https://webdocs.cs.ualberta.ca/~sutton/book/bookdraft2016sep.pdf
• mirror: https://pan.baidu.com/s/1slrMYkP
• github: https://github.com/ShangtongZhang/reinforcement-learning-an-introduction

The Self Learning Quant

• intro: explain and show the concept of self reinforcement learning combined with a neural network
• blog: https://medium.com/@danielzakrisson/the-self-learning-quant-d3329fcc9915#.9lsa5rh3e
• gihtub: https://github.com/danielzak/sl-quant

Reinforcement Learning: An Introduction

• author: Richard S. Sutton and Andrew G. Barto
• book: https://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html
• solutions: https://github.com/btaba/intro-to-rl

Resources

Deep Reinforcement Learning Papers

https://github.com/junhyukoh/deep-reinforcement-learning-papers

Awesome Reinforcement Learning

• website: http://aikorea.org/awesome-rl/?utm_content=buffer5d0f3&utm_medium=social&utm_source=plus.google.com&utm_campaign=buffer#online-demos
• github: https://github.com/aikorea/awesome-rl

Deep Reinforcement Learning Papers

• github: https://github.com/muupan/deep-reinforcement-learning-papers

Deep Reinforcement Learning 深度增强学习资源

• blog: https://zhuanlan.zhihu.com/p/20885568

deep-reinforcement-learning-networks: A list of deep neural network architectures for reinforcement learning tasks

• github: https://github.com/5vision/deep-reinforcement-learning-networks

Deep Reinforcement Learning survey

• github: https://github.com/andrewliao11/Deep-Reinforcement-Learning-Survey

Studying Reinforcement Learning Guide

• github: https://github.com/0bserver07/Study-Reinforcement-Learning

Reading and Questions

What are the best books about reinforcement learning?

https://www.quora.com/What-are-the-best-books-about-reinforcement-learning

Papers

Tutorials

Making a Contextual Recommendation Engine

• intro: by Muktabh Mayank
• youtube: https://www.youtube.com/watch?v=ToTyNF9kXkk&hd=1http://weibo.com/1402400261/profile?topnav=1&wvr=6
• video: http://pan.baidu.com/s/1eQFFVns

Papers

Collaborative Deep Learning for Recommender Systems

• arxiv: https://arxiv.org/abs/1409.2944
• paper: http://www.wanghao.in/paper/KDD15_CDL.pdf

Image-based recommendations on styles and substitutes

• paper: http://cseweb.ucsd.edu/~jmcauley/pdfs/sigir15.pdf
• code: http://cseweb.ucsd.edu/~jmcauley/code/imageGraph.tar.gz
• data: http://jmcauley.ucsd.edu/data/amazon/

A Complex Network Approach for Collaborative Recommendation

• arxiv: http://arxiv.org/abs/1510.00585v1

Session-based Recommendations with Recurrent Neural Networks

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06939
• github: https://github.com/hidasib/GRU4Rec

Item2Vec: Neural Item Embedding for Collaborative Filtering

• arxiv: https://arxiv.org/abs/1603.04259

Wide & Deep Learning for Recommender Systems

• intro: Google Research
• arxiv: http://arxiv.org/abs/1606.07792
• blog: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

Hybrid Recommender System based on Autoencoders

• arxiv: https://arxiv.org/abs/1606.07659
• github: https://github.com/fstrub95/Autoencoders_cf
• notes: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2016/06/hybrid-recommender-system-based-on-autoencoders.md

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

• paper: https://alexiskz.files.wordpress.com/2016/06/feature-rnn-paper1.pdf

Collaborative Filtering with Recurrent Neural Networks

• keywords: LSTM, movie recommendation
• arixv: http://arxiv.org/abs/1608.07400

Deep Neural Networks for YouTube Recommendations

• intro: RECSYS 2016. Google
• paper: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
• summary: https://blog.acolyer.org/2016/09/19/deep-neural-networks-for-youtube-recommendations/

Photo Filter Recommendation by Category-Aware Aesthetic Learning

• intro: Filter Aesthetic Comparison Dataset (FACD): 28,000 filtered images and 42,240 reliable image pairs with aesthetic comparison annotations
• arxiv: http://arxiv.org/abs/1608.05339

Convolutional Matrix Factorization for Document Context-Aware Recommendation

• project page: http://dm.postech.ac.kr/~cartopy/ConvMF/
• paper: http://dl.acm.org/citation.cfm?id=2959165

Deep learning for audio-based music recommendation

• slides: https://docs.google.com/presentation/d/1CRSAs2WOKo5mFhh5Iu-xkDfyJsg_NDL1r5dRtj6_aHo/edit#slide=id.p
• mirror: https://pan.baidu.com/s/1o8NaMPs

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

• arxiv: https://arxiv.org/abs/1609.02116

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1611.00454

Recurrent Recommender Networks

• intro: University of Texas at Austin & Google Research & CMU & LinkedIn
• paper: http://alexbeutel.com/papers/rrn_wsdm2017.pdf

Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

• intro: Visnet. Flipkart’s visual search and recommendation system
• arxiv: https://arxiv.org/abs/1703.02344
• github: https://github.com/flipkart-incubator/fk-visual-search

What Your Image Reveals: Exploiting Visual Contents for Point-of-Interest Recommendation

• intro: Arizona State University & Michigan State University
• intro: Point-of-Interest (POI)
• paper: http://www.public.asu.edu/~swang187/publications/VPOI.pdf

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

• intro: Gravity R&D & Telefonica Research
• arxiv: https://arxiv.org/abs/1706.03847
• github: https://github.com/hidasib/GRU4Rec

On Sampling Strategies for Neural Network-based Collaborative Filtering

• intro: KDD 2017. University of California, Los Angeles & Yahoo! Research & Etsy Inc
• arxiv: https://arxiv.org/abs/1706.07881

Deep Learning based Recommender System: A Survey and New Perspectives

• intro: University of New South Wales & Nanyang Technological University
• arxiv: https://arxiv.org/abs/1707.07435

Training Deep AutoEncoders for Collaborative Filtering

• arxiv: https://arxiv.org/abs/1708.01715
• github: https://github.com/NVIDIA/DeepRecommender

Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback

• intro: Zhejiang University
• arxiv: https://arxiv.org/abs/1712.09043

Deep Reinforcement Learning for List-wise Recommendations

• intro: Michigan State University & Data Science Lab
• arxiv: https://arxiv.org/abs/1801.00209

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

• intro: Michigan State University & JD.com
• arxiv: https://arxiv.org/abs/1802.06501

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

• intro: WSDM 2018. Simon Fraser University
• arxiv: https://arxiv.org/abs/1809.07426
• github(Matlab+MatcConvNet): https://github.com/graytowne/caser

Slides

Deep learning for music recommendation

• sldies: http://pan.baidu.com/s/1skriMJj

Deep learning for music recommendation and generation

• slides: https://docs.google.com/presentation/d/1AIotiiAp_528R90ll8j-Kc2EsRk2Oxc1poRgPXnEH8Y/edit
• mirror: https://pan.baidu.com/s/1czQQNO

Blogs

Recommending music on Spotify with deep learning

http://benanne.github.io/2014/08/05/spotify-cnns.html

Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE

http://blogs.aws.amazon.com/bigdata/post/TxGEL8IJ0CAXTK/Generating-Recommendations-at-Amazon-Scale-with-Apache-Spark-and-Amazon-DSSTNE

Recommending movies with deep learning

• blog: http://blog.richardweiss.org/2016/09/25/movie-embeddings.html
• ipn: https://github.com/ririw/ririw.github.io/blob/master/assets/Recommending%20movies.ipynb

Deep Learning Helps iHeartRadio Personalize Music Recommendations

• blog: https://news.developer.nvidia.com/deep-learning-helps-iheartradio-personalize-music-recommendations/

Applying deep learning to Related Pins

• intro: Pinterest
• blog: https://engineering.pinterest.com/blog/applying-deep-learning-related-pins

Recommendation System Algorithms: Main existing recommendation engines and how they work

https://blog.statsbot.co/recommendation-system-algorithms-ba67f39ac9a3

Building a Music Recommender with Deep Learning

• intro: Music recommender using deep learning with Keras and TensorFlow
• blog: http://mattmurray.net/building-a-music-recommender-with-deep-learning/
• github: https://github.com/mattmurray/music_recommender

Projects

NNRec: Neural models for Collaborative Filtering

• intro: Source code for, AutoRec, an autoencoder based model for collaborative filtering. This package also includes implementation of RBM based collaborative filtering model(RBM-CF).
• github: https://github.com/mesuvash/NNRec

Deep learning recommend system with TensorFlow

• intro: a general project to walk through the proceses of using TensorFlow
• github: https://github.com/tobegit3hub/deep_recommend_system

Deep Learning Recommender System

• github: https://github.com/freegraphics/MIDS

Keras Implementation of Recommender Systems

https://github.com/sonyisme/keras-recommendation

Videos

Deep Learning for Recommender Systems

• youtube: https://www.youtube.com/watch?v=KZ7bcfYGuxw

Using MXNet for Recommendation Modeling at Scale

• youtube: https://www.youtube.com/watch?v=cftJAuwKWkA
• mirror: https://pan.baidu.com/s/1kVsdrmR

Resources

Recommender Systems with Deep Learning

https://amundtveit.com/2016/11/20/recommender-systems-with-deep-learning/

Deep-Learning-for-Recommendation-Systems

• intro: This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
• github: https://github.com/robi56/Deep-Learning-for-Recommendation-Systems

Papers

Leaderboard

Papers

Papers