Playing Atari with Deep Reinforcement Learning

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We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.

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Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind

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Q-learning for Keras

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github.com: /Gary-Shi/Tank

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AI Program for Tank Using Separately Deep-Q-Learning and RL+MCTS

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Reinforcement learning with a convolutional neural network.

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A very simple version of playing Atari with Deep Q Learning in caffe(python interface)

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Github: natsumeS/AI

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

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github.com: /sygi/deep_q_rl

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Theano-based implementation of Deep Q-learning

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A toy AC Network built with curiosity through intrinsic reward

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Julia implementation of DeepMind's Deep Q Learning algorithm described in "Playing Atari with Deep Reinforcement Learning"

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Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

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Using implementation of Q-Learning and Pygame to simulate a self-driving car.

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Rainbow: Combining Improvements in Deep Reinforcement Learning

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github.com: /jonaths/tf-dqn

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Variation of "Asynchronous Methods for Deep Reinforcement Learning" with multiple processes generating experience for agent (Keras + Theano + OpenAI Gym)[1-step Q-learning, n-step Q-learning, A3C]

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Persistent advantage learning dueling double DQN for the Arcade Learning Environment

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Bot for Minecraft environment

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TensorFlow implementation of Deep Reinforcement Learning papers

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Github: tokb23/dqn

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DQN implementation in Keras + TensorFlow + OpenAI Gym

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My attempt to create a single AI that can play several games.

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Deep Reinforcement Learning for Keras.

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Deep Learning track / hackdays

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github.com: /JonasRSV/DQN

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Reinforcement Learning

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Lecture on Multivariate Classification for the CERN School of Computing

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Using reinforcement learning to teach a car to avoid obstacles.

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Implements basic reinforcement learning algorithms to control a remote-control car in a room.

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Implementation of a Dueling Double Deep Q-value Network with tensorflow

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Contains implementations of various deep RL algorithms and papers including action conditional video prediction | Python | Tensorflow | Open AI gym

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Intelligent traffic control on Vissim by dqn

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Re-implementation of method in Playing Atari with Deep Reinforcement Learning paper.

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Simple Deep Q Network in R

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Simple example of DQN for Unity using Keras

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super mario bot

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Github: mikhug/test

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A reinforcement learning agent that uses Deep Q Learning with Experience Replay to learn how to play Pong.

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Implementation of Deep Q-Network with TensorFlow

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Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind: Forked from kristjankorjus/Replicating-DeepMind

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Repository for all Honors Senior Thesis work. Research links and code should be kept here.

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github.com: /alfredvc/paac

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Open source implementation of the PAAC algorithm presented in Efficient Parallel Methods for Deep Reinforcement Learning

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use a deep-Q network to learn a policy to cross busy intersections

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Simple game playing with reinforcement learning

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Implementation of "Playing Atari with Deep Reinforcement Learning" with Tensorflow

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This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

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An implementation of Q algorithm and its deep variant for Reinforcement Learning purposes.

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Reinforcement learning course at Data Science Retreat

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Theano-based implementation of Deep Q-learning

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A tiny implementation of Deep Q Learning, using TensorFlow and OpenAI gym

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Diferentes aplicações de algoritmos de inteligência artificial (Machine Learning e Deep Learning).

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This repository contains Deep Q-Networks and Double DQN implementation in tensorflow for Open AI Gym environments.

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Python module of DQN agent for RL-GLUE

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Tensorflow implementation of deep Q networks in paper 'Playing Atari with Deep Reinforcement Learning'

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github.com: /R-Stefano/DQN

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Implementation of Deep Q-Network (DQN) on OpenAI games: Pong and Breakout using Tensorflow and Numpy

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Self Driving Car Demo for Fresh Machine Learning #6

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github.com: /jonaths/dqn-grid

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Training a Neural Network to play Pong from pixels

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implement '2013-Playing Atari with Deep Reinforcement Learning'

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Implementation of Google's paper on playing atari games using deep learning in python.

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github.com: /ninja18/AtariDQN

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Deep Q Learning model to play atari games based on the paper http://arxiv.org/pdf/1312.5602v1.pdf

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