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/KaihuaTang/Scene-Graph-Benchmark.pytorch

Learning to Compose Dynamic Tree Structures for Visual Contexts

https://github.com/KaihuaTang/VCTree-Scene-Graph-Generation

Learning to Compose Dynamic Tree Structures for Visual Contexts

https://github.com/KaihuaTang/VCTree-Visual-Question-Answering

Learning to Compose Dynamic Tree Structures for Visual Contexts

https://github.com/Kaixhin/ACER

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Kaixhin/EC

Model-Free Episodic Control

https://github.com/Kaixhin/NoisyNet-A3C

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Kaixhin/Rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/KaiyangZhou/pytorch-vsumm-reinforce

Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

https://github.com/KamalM8/Few-Shot-learning-Fashion

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

https://github.com/Karim-53/SGG

Learning to Compose Dynamic Tree Structures for Visual Contexts

https://github.com/KarlXing/RL-Visual-Continuous-Control

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

https://github.com/KarlXing/gym

OpenAI Gym

https://github.com/KarolisRam/colour-shape-goal-misgeneralization

Goal Misgeneralization in Deep Reinforcement Learning

https://github.com/KatyNTsachi/Hierarchical-RL

Playing Atari with Deep Reinforcement Learning

https://github.com/Kaushal28/Bengali-AI

GridMask Data Augmentation

https://github.com/KavindaKottege/DeepQ-Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/Kchu/DeepRL_CK

A Distributional Perspective on Reinforcement Learning

https://github.com/KeatonMueller/rl-cube

Solving the Rubik's Cube Without Human Knowledge

https://github.com/KellyYutongHe/Knowledge-Distillation-Net-with-Swish

Searching for Activation Functions

https://github.com/KelvinYang0320/deepbots-panda

Continuous control with deep reinforcement learning

https://github.com/Kerram/Deephol-Bert-Zpp

HOList: An Environment for Machine Learning of Higher-Order Theorem Proving

https://github.com/Kerram/holist-train

HOList: An Environment for Machine Learning of Higher-Order Theorem Proving

https://github.com/KhenNguyn/DoAn3-MachineLearning

Hybrid Reward Architecture for Reinforcement Learning

https://github.com/Khrylx/PyTorch-RL

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Kikumu/Reinforcement-Learning-repo

Continuous control with deep reinforcement learning

https://github.com/KlabCMU/ESNAC

Learnable Embedding Space for Efficient Neural Architecture Compression

https://github.com/KnightZhang625/DARTS_NAS_TextClassification

DARTS: Differentiable Architecture Search

https://github.com/KohMat/carracing-dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/KristofPusztai/MixReg

Improving Generalization in Reinforcement Learning with Mixture Regularization

https://github.com/KshamaDw/collaborative-competitive-games

Generative Adversarial Imitation Learning

https://github.com/Kumar-Tarun/summarization

Self-critical Sequence Training for Image Captioning

https://github.com/Kunal-Kumar-Sahoo/Deep-Reinforcement-Learning-with-Human-Preferences

Deep reinforcement learning from human preferences

https://github.com/KunlinY/darts

DARTS: Differentiable Architecture Search

https://github.com/KunlinY/darts-parallel

DARTS: Differentiable Architecture Search

https://github.com/KyleChen400/patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/L0SG/seqgan-music

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

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