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Deep Reinforcement Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/MIC-DKFZ/TractSeg

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/MLCS-Yonsei/ddpg-control

Continuous control with deep reinforcement learning

https://github.com/MLforHealth/rl_representations

Benchmarking Batch Deep Reinforcement Learning Algorithms

https://github.com/MOVzeroOne/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/MVerghese/NN-Dynamics-Sparse-Reward

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/MYPETFISH/PGPortfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/ManooshSamiei/Object-Detection-Deep-Reinforcement-Learning

Object Detection with Deep Reinforcement Learning

https://github.com/MarsEleven/car_racer_RL

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/MateuszJanda/netris-ai-robot

Playing Atari with Deep Reinforcement Learning

https://github.com/MathPhysSim/PER-NAF

Continuous control with deep reinforcement learning

https://github.com/MatheusMRFM/A3C-LSTM-with-Tensorflow

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/MattJud/gnn_jssp

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

https://github.com/MattKleinsmith/pbt

Population Based Training of Neural Networks

https://github.com/MatthieuSarkis/Portfolio-Optimization-and-Goal-Based-Investment-with-Reinforcement-Learning

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/MaxSobolMark/HardRLWithYoutube

Playing hard exploration games by watching YouTube

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Deep_Reinforcement_Learning_with_Double_Q_learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Playing_Atari_with_Deep_Reinforcement_Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/Medabid1/RL_Project

Continuous control with deep reinforcement learning

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Playing Atari with Deep Reinforcement Learning

https://github.com/MehranTaghian/DQN-Trading

Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning

https://github.com/MikulasZelinka/pyfiction

Language Understanding for Text-based Games Using Deep Reinforcement Learning

https://github.com/Mind23-2/MindCode-152

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/MindCode-4/code-8/tree/main/Neural-Network-based-Speech-Enhancement

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/MindSpore-paper-code-3/code5/tree/main/RNA

Learning to Design RNA

https://github.com/MindSpore-scientific-2/code-4/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific-2/code-5/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific-2/code-8/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-10/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-13/tree/main/PCGrad-mindspore-example

Gradient Surgery for Multi-Task Learning

https://github.com/MindSpore-scientific/code-8/tree/main/Simba

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

https://github.com/MoMe36/BranchingDQN

Action Branching Architectures for Deep Reinforcement Learning

https://github.com/MohammadAsadolahi/DDQN_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Deep Reinforcement Learning with Double Q-learning

https://github.com/MohammadAsadolahi/Dueling_Deep-Double_Q-Learning-for-solving-OpenAi-Gym-LunarLander-v2-in-python

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/Montherapy/Deep-reinforcement-learning-for-multi-class-imbalanced-classification

Deep Reinforcement Learning with Double Q-learning

https://github.com/MotorCityCobra/C_plusplus_mpo

Maximum a Posteriori Policy Optimisation

https://github.com/MrDaubinet/collaboration-and-competition

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.