Awesome Deep RlA curated list of awesome Deep Reinforcement Learning resources.
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Naf Tensorflow"Continuous Deep Q-Learning with Model-based Acceleration" in TensorFlow
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Pytorch sacPyTorch implementation of Soft Actor-Critic (SAC)
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Machine Learning Is All You Need🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
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DrlRepository for codes of 'Deep Reinforcement Learning'
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Noisynet A3cNoisy Networks for Exploration
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Deep Reinforcement Learning Algorithms31 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.
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Accel Brain CodeThe purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.
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Gym CarlaAn OpenAI gym wrapper for CARLA simulator
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AirsimOpen source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
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Spinning Up BasicBasic versions of agents from Spinning Up in Deep RL written in PyTorch
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Awesome Deep NeuroevolutionA collection of Deep Neuroevolution resources or evolutionary algorithms applying in Deep Learning (constantly updating)
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Go Bot DrlGoal-Oriented Chatbot trained with Deep Reinforcement Learning
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MinimalrlImplementations of basic RL algorithms with minimal lines of codes! (pytorch based)
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Baby A3cA high-performance Atari A3C agent in 180 lines of PyTorch
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ScalphagozeroAn independent implementation of DeepMind's AlphaGoZero in Scala, using Deeplearning4J (DL4J)
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PcgradCode for "Gradient Surgery for Multi-Task Learning"
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