Papers with Code 아카이브

Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

162

보존된 평가 기록

1

코드

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

https://github.com/LAS1520/Gato-A-Generalist-Agent

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/LKLQQ/LEO

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

https://github.com/LM095/DDPG-implementation

Continuous control with deep reinforcement learning

https://github.com/LMikeH/ocbnn-lmikh

Weight Uncertainty in Neural Networks

https://github.com/LXYYY/multiagent-particle-envs

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

https://github.com/Lagostra/exprimo

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

https://github.com/Lamperougeyxy/GHQ

The StarCraft Multi-Agent Challenge

https://github.com/LarreaMikel/bsuite_tutorial

Behaviour Suite for Reinforcement Learning

https://github.com/LauraGentini/QRL

Quantum reinforcement learning

https://github.com/Lausannen/NAS-FCOS

NAS-FCOS: Fast Neural Architecture Search for Object Detection

https://github.com/LeejwUniverse/RL_Exploration_Pytorch

Exploration by Random Network Distillation

https://github.com/LeoToledo/PivotingTaskRL

Reinforcement Learning for Pivoting Task

https://github.com/LeoZhengZLY/vertex-net

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks

https://github.com/Lham71/Maximising-Neural-Information-Processing

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning

https://github.com/LiangqunLu/DLForChatbot

Deep Reinforcement Learning for Dialogue Generation

https://github.com/Liberty3000/rl

Relational Deep Reinforcement Learning

https://github.com/LilTwo/DRL-using-PyTorch

Deep Q-learning from Demonstrations

https://github.com/Linear95/CLUB

Representation Learning with Contrastive Predictive Coding

https://github.com/Linging/Traffic-Signal-Control

Playing Atari with Deep Reinforcement Learning

https://github.com/LiorAl/GymValueIterationNetworks

Value Iteration Networks

https://github.com/LoganZhao1997/last_mile_challenge

Learning Combinatorial Optimization Algorithms over Graphs

https://github.com/Loriba4/Lori-Baxter

Learning Combinatorial Optimization Algorithms over Graphs

https://github.com/Lorna-Liu/ultrasound_vsumm_RL

Ultrasound Video Summarization using Deep Reinforcement Learning

https://github.com/Louiii/ValueDecomposition

Value-Decomposition Networks For Cooperative Multi-Agent Learning

https://github.com/LuEE-C/PPO-Keras

Proximal Policy Optimization Algorithms

https://github.com/LuK2019/ICNN_BA

Input Convex Neural Networks

https://github.com/LuanAdemi/VisualGo

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

https://github.com/LucaMaurici/MultiHop-Reading-Comprehension-DRL

Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal

https://github.com/LucasAlegre/sac-plus

Soft Actor-Critic Algorithms and Applications

https://github.com/LucasElbert/GradCam

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

https://github.com/LucasSilve/openAIgym

OpenAI Gym

https://github.com/LukasGardberg/cartpole

Playing Atari with Deep Reinforcement Learning

https://github.com/Lyusungwon/apex_dqn_pytorch

Distributed Prioritized Experience Replay

https://github.com/M46N3/learn2drive

OpenAI Gym

https://github.com/MEOWMEOW114/nd893-p1-navigation-banana

Deep Reinforcement Learning with Double Q-learning

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

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.