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/Alina9/SAC

Soft Actor-Critic Algorithms and Applications

https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle

A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks

https://github.com/AlirezaShamsoshoara/Multi-Independent-Agent-Reinforcement-Learning-UAV-Autonomous-Spectrum-QLearning

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations

https://github.com/AlirezaShamsoshoara/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task

A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks

https://github.com/AlvinIsonomia/darts.pytorch1.3_MultiGPU

DARTS: Differentiable Architecture Search

https://github.com/Amadeus979/decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Ambitioner-c/UserSimulator

A User Simulator for Task-Completion Dialogues

https://github.com/AmeerHajAli/ray2

Ray: A Distributed Framework for Emerging AI Applications

https://github.com/AmirNiaraki/Visual-Exploration

Visual Exploration and Energy-aware Path Planning via Reinforcement Learning

https://github.com/AndrewJWashington/protodriver

Playing Atari with Deep Reinforcement Learning

https://github.com/AndrewSukhobok95/DL_GraphEntity_project

A Hierarchical Framework for Relation Extraction with Reinforcement Learning

https://github.com/AndreyKolomiets/News_Headline_Generation

A Deep Reinforced Model for Abstractive Summarization

https://github.com/Andy-Dang12/Activation-Functions

Searching for Activation Functions

https://github.com/AnejSvete/gym

OpenAI Gym

https://github.com/Angeliki-Panoulia/CallForCode

Scalable agent alignment via reward modeling: a research direction

https://github.com/Anjaney1999/image-captioning-seqgan

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/Ankush96/grad-cam.tensorflow

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

https://github.com/AnnaNikitaRL/EVA

Fast deep reinforcement learning using online adjustments from the past

https://github.com/Anou9531/Bayesian-CNN

Weight Uncertainty in Neural Networks

https://github.com/AnshMittal1811/AugmentedRandomSearch

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/Anshu1245/RL-CourseProject

Playing Atari with Deep Reinforcement Learning

https://github.com/Antonildo43/Projeto-Construcao-do-Algoritmo-FOP

First-Order Preconditioning via Hypergradient Descent

https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch

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

https://github.com/AppliedDataSciencePartners/WorldModels

World Models

https://github.com/Aravind-11/AI-Gaming

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/IITM_Saastra

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/Multi-Agent-RL

Proximal Policy Optimization Algorithms

https://github.com/ArayCHN/HAAR-A-Hierarchical-RL-Algorithm

Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

https://github.com/Archibald-Lafraik/pipps

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos

https://github.com/ArchieGertsman/spark-sched-sim

Learning Scheduling Algorithms for Data Processing Clusters

https://github.com/ArnaudFickinger/adversarial-surprise

Explore and Control with Adversarial Surprise

https://github.com/ArnoutDevos/maml-cifar-fs

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

https://github.com/Arrabonae/openai_DDDQN

Prioritized Experience Replay

https://github.com/Asap7772/OfflineRlWorkflow

A Workflow for Offline Model-Free Robotic Reinforcement Learning

https://github.com/Asap7772/understanding-rlhf

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/Ashish-Tripathy/AI_SelfDriving

Model-free Deep Reinforcement Learning for Urban Autonomous Driving

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