aistrych

rllab

rllab is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym.

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

RecurrentHighwayNetworks

Recurrent Highway Networks - Implementations for Tensorflow, Torch7, Theano and Brainstorm

keras-rl

Deep Reinforcement Learning for Keras.

residual_block_keras

Residual network block in Keras

A3C

Advantage async actor-critic Algorithms (A3C) and Progressive Neural Network implemented by tensorflow.

tensorflow_with_latest_papers

Implementation of Newest RNN and Seq2Seq Features

tf-agent

tensorflow reinforcement learning agents for OpenAI gym environments

DNI-tensorflow

DNI(Decoupled Neural Interfaces using Synthetic Gradients) implementation with Tensorflow

Atari

Persistent advantage learning dueling double DQN for the Arcade Learning Environment

ufcnn-keras

Implementation of UFCNN in Keras

async_deep_reinforce

Asynchronous Methods for Deep Reinforcement Learning

tensorflow-synthetic_gradient

highway-fcn

Simple fully-connected highway networks using TensorFlow and Fomoro.

highway-cnn

Simple convolutional highway networks using TensorFlow and Fomoro.

deep_trader

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.

ufcnn

Implementation of Undecimated Fully Convolutional Neural Network for time series modeling

keras-resnet

Residual networks implementation using Keras-1.0 functional API

async_deep_reinforce-1

A3C implementation which trains an agent for a small MDP.

RAM

"Recurrent Models of Visual Attention" in TensorFlow

snli-entailment

attention model for entailment on SNLI corpus

async-rl

Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"

genstake-newer

Genstake - fork for historic purposes ;)

Associative_LSTM

LSTM with associative memory cells (http://arxiv.org/abs/1602.03032)

Imaging-time-series-to-improve-classification-and-imputation

Init creation

Asynchronous-Methods-for-Deep-Reinforcement-Learning

Using a paper from Google DeepMind I've developed a new version of the DQN using threads exploration instead of memory replay as explain in here: http://arxiv.org/pdf/1602.01783v1.pdf I used the one-step-Q-learning pseudocode, and now we can train the Pong game in less than 20 hours and without any GPU or network distribution.

resnet-tf

ResNet Implementation in TensorFlow

DARQN

Deep Attention Recurrent Q-Network

DropoutUncertaintyDemos

What My Deep Model Doesn't Know...

highway-networks

An implementation of Highway Networks in Caffe