Papers with Code 아카이브

Reinforcement Learning

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코드

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

https://github.com/anonDEHB/DEHB

Regularized Evolution for Image Classifier Architecture Search

https://github.com/anonNo2/MulTeacher-KD

DARTS: Differentiable Architecture Search

https://github.com/anonymous1234517/code

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/anoxia-1/Fault-Tolerant-Federated-Reinforcement-Learning-with-Theoretical-Guarantee-

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

https://github.com/antaradas94/MAML-waste-classification

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/anthonysicilia/leather-aacl2022

Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat

https://github.com/anthonysong98/super-mario-bros-ppo

Proximal Policy Optimization Algorithms

https://github.com/antoine-doolaeghe/new-born-unity-scripts

Unity: A General Platform for Intelligent Agents

https://github.com/anuragajay/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/anyboby/ConstrainedMBPO

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/apollopower/DOOM-AI

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/apoorvkhattar/RL-Project-2019

RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion

https://github.com/apourchot/ERL

Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/apourchot/ERL-pytorch

Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/apourchot/EvoLearn

Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/aqzheng/Adversarial-Learning-for-Neural-Dialogue-Generation

Adversarial Learning for Neural Dialogue Generation

https://github.com/araffin/learning-to-drive-in-5-minutes

Learning to Drive in a Day

https://github.com/araffin/sbx

Proximal Policy Optimization Algorithms

https://github.com/araffin/srl-zoo

Time-Contrastive Networks: Self-Supervised Learning from Video

https://github.com/aragakiyuiii/Gumbel-darts-master

DARTS: Differentiable Architecture Search

https://github.com/aravic/generalizable-device-placement

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

https://github.com/aravind0706/neural-mpc

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

https://github.com/aravindsrinivas/curl_rainbow

When to use parametric models in reinforcement learning?

https://github.com/aravindsrinivas/neural-mpc

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

https://github.com/aravindsrinivas/upn

Universal Planning Networks

https://github.com/arazd/ProgressivePrompts

Progressive Neural Networks

https://github.com/archit120/Risk-Aware-Active-IRL

Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning

https://github.com/arcosin/Doric

Progressive Neural Networks

https://github.com/aredier/monte_carlo_dropout

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

https://github.com/argmax-ai/drone-hardware

Learning to Fly via Deep Model-Based Reinforcement Learning

https://github.com/aricsvenz/AI_for_Drugs

ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

https://github.com/arjung128/image-paragraph-captioning

Self-critical Sequence Training for Image Captioning

https://github.com/arneschmidt/bayesian_deep_learning

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

https://github.com/arnomoonens/yarll

Continuous control with deep reinforcement learning

https://github.com/arobey1/LipSDP

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks

https://github.com/arodriguezca/uncertainty-ts-forecasting

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

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