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/wgchang/PCGrad-pytorch-example

Gradient Surgery for Multi-Task Learning

https://github.com/wgrathwohl/BackpropThroughTheVoidRL

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

https://github.com/wh1992v/R2RRMopionSolitaire

Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization

https://github.com/willspag/SeqGan

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/willwhitney/dynamics-aware-embeddings

Dynamics-aware Embeddings

https://github.com/willwhitney/pytorch_sac_lib

Soft Actor-Critic Algorithms and Applications

https://github.com/windstrip/DeepMind-StreetLearn

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/windstrip/PGPortfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/wineslab/colosseum-near-rt-ric

ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms

https://github.com/wineslab/ns-o-ran-e2-sim

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

https://github.com/wmn7/universal_light

ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning

https://github.com/wmol4/Pytorch_DDQN_Unity_Navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/wnorowsb/Super-resolution-GAN

Searching for Activation Functions

https://github.com/wongongv/mbpo_fork

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/wongongv/scnwithdqn

Structured Control Nets for Deep Reinforcement Learning

https://github.com/worldanon/learntopredict

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

https://github.com/wpiszlogin/driver_critic

Continuous control with deep reinforcement learning

https://github.com/wronnyhuang/SQLNet_inference

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/wsanchezdupont1/Grad-CAM

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

https://github.com/wsjeon/multiagent-particle-envs-maac

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

https://github.com/wsjeon/multiagent-particle-envs-v2

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

https://github.com/wtingda/DeepRLBreakout

Deep Reinforcement Learning with Double Q-learning

https://github.com/wtliao/ImageTransformer

Self-critical Sequence Training for Image Captioning

https://github.com/wu375/spiral-pytorch-ray

Synthesizing Programs for Images using Reinforced Adversarial Learning

https://github.com/wu375/spiral_pytorch_ray

Synthesizing Programs for Images using Reinforced Adversarial Learning

https://github.com/wuxx1016/Reinforcement-Learning-in-Finance

QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds

https://github.com/wvabrinskas/Neuron

Searching for Activation Functions

https://github.com/wwydmanski/rl_tennis

Continuous control with deep reinforcement learning

https://github.com/wxj77/TransferReinforcementLearning

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/wyattlansford/MineRL

MOReL : Model-Based Offline Reinforcement Learning

https://github.com/wyndwarrior/imitation_from_observation

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/wyz2368/open_spiel2

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/wyz2368/open_spiel3

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/wyz2368/open_spiel_egta

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/wzlxjtu/PositionalEncoding2D

Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training

https://github.com/xValentim/Steering_Behaviors_with_pygame

Playing Atari with Deep Reinforcement Learning

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