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/YaCpotato/deepaugmentFix

Neural Architecture Search with Reinforcement Learning

https://github.com/YadiLao/MM-Tag

A Tree Search Algorithm for Sequence Labeling

https://github.com/YanSong97/Master-thesis

Noisy Networks for Exploration

https://github.com/YangRui2015/Modular_HER

Continuous control with deep reinforcement learning

https://github.com/Yangangren/Adversarial-Reinforcement-learning

Robust Adversarial Reinforcement Learning

https://github.com/Yangangren/Adversarial_RL

Robust Adversarial Reinforcement Learning

https://github.com/YanglanWang/classic_control

OpenAI Gym

https://github.com/YaoMarkMu/DRQTRANS

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

https://github.com/Yarlak/Larry

Unity: A General Platform for Intelligent Agents

https://github.com/Yarlak/LarryOriginal

Unity: A General Platform for Intelligent Agents

https://github.com/YicongHong/Entity-Graph-VLN

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

https://github.com/YicongHong/Recurrent-VLN-BERT

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

https://github.com/YilongJu/MolGAN

MolGAN: An implicit generative model for small molecular graphs

https://github.com/Yizhao111/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/YoUNG824/DDPG

Continuous control with deep reinforcement learning

https://github.com/Youngb2n/GradCAM

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

https://github.com/YuansongFeng/MadMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/YufanPaPa/GAN_SSN

Adversarial Learning for Neural Dialogue Generation

https://github.com/YuhangSong/Mega-Reward

Deep Reinforcement Learning that Matters

https://github.com/YushiChen/Auto-CNN-HSI-Classification

DARTS: Differentiable Architecture Search

https://github.com/Yuta123456/poker-ai

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/Yutongamber/MADDPG

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

https://github.com/ZD1wang/Traffiti

An Efficient Deep Reinforcement Learning Model for Urban Traffic Control

https://github.com/ZHZisZZ/modpo

Disentangling Length from Quality in Direct Preference Optimization

https://github.com/ZTao-z/ProxylessNAS

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

https://github.com/ZachZhu7/banana-git

BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search

https://github.com/ZacharyChenpk/Local_ETHZ

Learning Dynamic Context Augmentation for Global Entity Linking

https://github.com/ZachisGit/LearningFromHumanPreferences

Deep reinforcement learning from human preferences

https://github.com/Zackory/assistive-gym-fem

Assistive Gym: A Physics Simulation Framework for Assistive Robotics

https://github.com/ZainRaza14/Neuro-Evolution

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/ZainRaza14/deepRL

Continuous control with deep reinforcement learning

https://github.com/Zartris/TD3_continuous_control

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Zartris/TD3_multi_agent_tennis

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

https://github.com/Zeta36/chess-alpha-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/Zhendong-Wang/arsm_image_captioning

Self-critical Sequence Training for Image Captioning

https://github.com/ZhenyueQin/Implementation-MolGAN-PyTorch

MolGAN: An implicit generative model for small molecular graphs

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