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/ARISE-Initiative/robosuite-benchmark

Soft Actor-Critic Algorithms and Applications

https://github.com/ASzot/ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/AWLyrics/SeqGAN_Poem

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/AashrayAnand/rubiks-cube-reinforcement-learning

Solving the Rubik's Cube Without Human Knowledge

https://github.com/Abdelhamid-bouzid/Distributional-RL

A Distributional Perspective on Reinforcement Learning

https://github.com/Abdelhamid-bouzid/Multi-Agent-Deep-Deterministic-Policy-Gradient

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

https://github.com/Abhaya1998/Self-Driving-Car

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/Acciorocketships/pymarl2

Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning

https://github.com/Acmece/rl-collision-avoidance

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning

https://github.com/AcutronicRobotics/gym-gazebo2

gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo

https://github.com/AdamStelmaszczyk/dqn

Hindsight Experience Replay

https://github.com/AdeelMufti/WorldModels

World Models

https://github.com/Adeikalam/Go-Explore

Go-Explore: a New Approach for Hard-Exploration Problems

https://github.com/Adlik/model_optimizer

AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

https://github.com/Adrelf/DRL-navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/Aedelon/fair-efficient_network

Learning Fairness in Multi-Agent Systems

https://github.com/AemikaChow/DATASOURCE

Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation

https://github.com/AgrawalAmey/safe-explorer

Continuous control with deep reinforcement learning

https://github.com/Ah31/maddpg_pytorch

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

https://github.com/AhmadQasim/proxylessnas-dense

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/AhnYoungBin/Grad_cam

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AidanRocke/variational_empowerment

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

https://github.com/AidanRocke/vertex_prediction

Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++

https://github.com/AimoreRRD/AIMORE_GAME

Towards Symbolic Reinforcement Learning with Common Sense

https://github.com/AlanYangYi/GayPhotoStyleRecongnizer

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AlanYangYi/PhotoStyleRecongnizer

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AldrichCabrera/gradCam-smallvgg

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AleXander-Tsui/MPE

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

https://github.com/Alescontrela/viper_rl

Mastering Diverse Domains through World Models

https://github.com/AlexChaloner/RL-project

Dopamine: A Research Framework for Deep Reinforcement Learning

https://github.com/AlexHeyman/PopulationBasedTraining

Population Based Training of Neural Networks

https://github.com/AlexSzen/wwf_shark_id

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/AlexZhaoZt/Temporal_Leap_HRL

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/AlexandraFilimokhina/MountainCarConrinious_DDPG

Continuous control with deep reinforcement learning

https://github.com/AliengirlLiv/xrl-dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/Alina-Samokhina/guided_cost_RL

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

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