Asynchronous Methods for Deep Reinforcement Learning

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We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.

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Simple game playing with reinforcement learning

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A deep reinforcement learning library in tensorflow

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Implementation of Asynchronous Methods for Deep Reinforcement Learning in TensorFlow + OpenAI Gym

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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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Reinforcement learning course at Data Science Retreat

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A Tensorflow based implementation of "Asynchronous Methods for Deep Reinforcement Learning": https://arxiv.org/abs/1602.01783

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Research project focusing on Deep Reinforcement.

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Deep Reinforcement Learning for Keras.

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Reinforcement learning models in ViZDoom environment

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Tensorflow implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".

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A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

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Examples of published reinforcement learning algorithms in recent literature implemented in TensorFlow

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github.com: /qihongl/dnd-lstm

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A pytorch implementation of LSTM cell with a differentiable neural dictionary, based on Ritter et al. (2018). Been There, Done That: Meta-Learning with Episodic Recall.

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Using deep reinforcement learning (DDPG & A3C) to solve Acrobot

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deep reinforcement learning on Atari games

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submission to TTI-Chicago programming requirement

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The architecture used to train the level generator in the game Relay.

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Modified tensorflow implementation of 'Asynchronous Methods for Deep Reinforcement Learning'

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synchronous actor critic for ATARI

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WBA Hackathon 2017 award winning codes

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A starter agent that can solve a number of universe environments.

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Asynchronous Methods for Deep Reinforcement Learning

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Distributed Tensorflow Implementation of Asynchronous Methods for Deep Reinforcement Learning

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Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783)

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TensorFlow implementation of Deep Reinforcement Learning papers

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Asynchronous Methods for Deep Reinforcement Learning

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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.

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Implementation of Attentive Multi Task Deep Reinforcement Learning Architecture in Tensorflow

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Asynchronous Advantage Actor-Critic (A3C) Algorithms implemented in TensorFlow 1.3

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github.com: /JulT1/RL_SS19

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Deep reinforcement learning package for torch7

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Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

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Teaching materials for the seminar on Application of Computational Intelligence Methods

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Tensorflow implementation of asyncronous 1-step Q learning in "Asynchronous Methods for Deep Reinforcement Learning" with improvement on weight update process (use minibatch) to speed up training.

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Asynchronous deep reinforcement learning

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Tensorflow implementation of 'Asynchronous Methods for Deep Reinforcement Learning'

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Implementation of asynchronous n-step Q-learning from Deepmind's "Asynchronous Methods for Deep Reinforcement Learning"

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Asynchronous Methods for Deep Reinforcement Learning

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a3c with theano and python multiprocessing for atari

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Playing Atari with QDN

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

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Code etc for Hacker Dojo Deep Learning Study Group

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This is a tensorflow implementation of asynchronous one step deep Q learning algorithm

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RL Study schedules. It includes plans, studies, projects, assignments

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Implementing Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning". using TensorFlow

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github.com: /sharan-dce/A3C

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An implementation of the Asynchronous Advantage Actor Critic (A3C) algorithm for any environment

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This repo is for policy gradient method

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PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".

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Implementation of "Asynchronous Methods for Deep Reinforcement Learning" by Deep Mind

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This repo demonstrates the usage of an actor-critic setup via the deep-deterministic-policy-gradients algorithm. The environment to be solved is the Unity Reacher Environment provided in the Udacity Deep Reinforcement Learning nanodegree

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A toy AC Network built with curiosity through intrinsic reward

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An implementation of a DQN to play Space Invaders using TensorFlow and Open-AI gym

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A starter agent that can solve a number of universe environments.

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Code for parallelizing your reinforcement learning algorithm

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github.com: /4rChon/NL-FuN

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N-Layered FeUdal Networks based on FeUdal Networks adapted to suit PySC2 observations

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Training a DRL agent to play Flappy Bird. An exercise to reimplement DQN, A2C, and PPO DRL methods.

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asynchronous advantage actor critic

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PyTorch implementation of Advantage async actor-critic Algorithms (A3C) in PyTorch

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Implementation of Asynchronous methods for deep reinforcement learning Breakout

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Training a DRL agent to play Flappy Bird. An exercise to reimplement DQN, A2C, and PPO DRL methods.

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Github: natsumeS/AI

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Learning to play supermario using A3C algorithm

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ai2thor cup picking using a3c

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Python implementation of tabular asynchronous actor critic

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1-step Q Learning from the paper "Asynchronous Methods for Deep Reinforcement Learning"

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My implementation of an A3C

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A3C Reinforcement Learner for the PysSC2 API

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Github: dm-mch/a3c

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Advanced actor critic

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Github: pqhuy98/A3C

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Async Advantage Actor Critic

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

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Actor-critic with experience replay

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Github: happywu/A3C

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MXNET + OpenAI Gym implementation of A3C from "Asynchronous Methods for Deep Reinforcement Learning"

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Simple Example A3C Reinforcement Learning Algorithm in Tensorflow

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A3C Algorithm for classic Atari games

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AI sonic project

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