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MIT DeepTraffic top 2% solution (75.01 mph) 🚗.

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Data Science & Machine Learning projects and tutorials in python from beginner to advanced level.

The purpose of our research is to study reinforcement learning approaches to building a movie recommender system. We formulate the problem of interactive recommendation as a contextual multi-armed bandit.

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Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

A working implementation of the Categorical DQN (Distributional RL).

강화학습에 대한 기본적인 알고리즘 구현

Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms

🙄 Difficult algorithm, Simple code.

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

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

Coursera Reinforcement Learning Specialization by University of Alberta & Alberta Machine Intelligence Institute

ROS 2 enabled Machine Learning algorithms

Lending Club Loan data analysis

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AIXIjs - General Reinforcement Learning in the Browser

Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout

Chess reinforcement learning by AlphaGo Zero methods.

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AI斗地主

Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

MATLAB library for non-negative matrix factorization (NMF): Version 1.8.1

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

31 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.

machine learning

AI research environment for the Atari 2600 games 🤖.

PyTorch implementation of Soft Actor-Critic (SAC)

Implementations of deep RL papers and random experimentation

A day to day plan for this challenge. Covers both theoritical and practical aspects

A library for ready-made reinforcement learning agents and reusable components for neat prototyping

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

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Log Anomaly Detection - Machine learning to detect abnormal events logs

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

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Deep Reinforcement Learning for de-novo Drug Design