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/sgondala/GoogleConceptualCaptioning

Self-critical Sequence Training for Image Captioning

https://github.com/sguo28/DROP_Simulator

DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning

https://github.com/shadowyzy/NIPS2019-MineRL-Competition-solution

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

https://github.com/shahabi8/Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/shahin-01/vqa-ad

Continuous control with deep reinforcement learning

https://github.com/shakedzy/warehouse

Continuous control with deep reinforcement learning

https://github.com/shakti365/nes

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/shamanez/Target-Driven-Visual-Navigation-with-Distributed-PPO

Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

https://github.com/shamanez/Variational-Discriminator-Bottleneck-Tensorflow-Implementation

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/shamanez/masters-work-target-driven-visual-navigation

VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation

https://github.com/shaneallcroft/SSB64RLBot

Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning

https://github.com/shantanusingh16/AlignSAM-CVPR2024-Unofficial-

AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning

https://github.com/shaohua0116/MultiDigitMNIST

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

https://github.com/sharan-dce/A3C

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/sharan-dce/DIAL-switch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

https://github.com/sharan-dce/coordination-pong

Multiagent Cooperation and Competition with Deep Reinforcement Learning

https://github.com/sharedcare/MARL-Cooperative

Prosocial learning agents solve generalized Stag Hunts better than selfish ones

https://github.com/shariqiqbal2810/AI-QMIX

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

https://github.com/shariqiqbal2810/maddpg-pytorch

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

https://github.com/shariqiqbal2810/multiagent-particle-envs

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

https://github.com/sharisun18/Absent_Supervisor_Env

AI Safety Gridworlds

https://github.com/shashankMadan-designEsthetics/ParlAICustom

ParlAI: A Dialog Research Software Platform

https://github.com/shashidhar100/Comparison-of-gradcam-and-gradcam_plus_plus_keras

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

https://github.com/shashist/recsys-rl

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

https://github.com/shashwatsaxena571/DRL-navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/sheetalreddy/One-shot-learning

Active One-shot Learning

https://github.com/shehroze37/Augmented-Deep-Reinforcement-Learning-for-3D-environments

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

https://github.com/shehrum/RL_Continous-Control

Continuous control with deep reinforcement learning

https://github.com/shehrum/RL_Navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/shekharRavi/Beyond-Task-Success-NAACL2019

Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat

https://github.com/sherry4186/Distributed-DQN

Distributed Prioritized Experience Replay

https://github.com/sheryl-ai/MetaPred

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

https://github.com/shinian123/gym

OpenAI Gym

https://github.com/sholtodouglas/pointMass

Learning Multi-Level Hierarchies with Hindsight

https://github.com/showkeyjar/chinese_im2text.pytorch

Stack-Captioning: Coarse-to-Fine Learning for Image Captioning

https://github.com/shreyasc-13/off_domain_rlkit

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

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