Papers with Code 아카이브

Decision Making

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/h-j-han/automatic_explicitation

Quizbowl: The Case for Incremental Question Answering

https://github.com/h2r/ImitateLearning-Movo

Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

https://github.com/h2r/parameterized-imitation-learning

Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation

https://github.com/h2r/sloop

Spatial Language Understanding for Object Search in Partially Observed City-scale Environments

https://github.com/hackthecrisis2020/sat-images

Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic

https://github.com/hamidreza-mazandarani/SAMA-D3QL

A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications

https://github.com/hammerlabml/diversesemifactualsreject

"Even if ..." -- Diverse Semifactual Explanations of Reject

https://github.com/hbzhang/AwesomeSelfDriving

Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge

https://github.com/hero9968/Graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/heshameraqi/Dynamic-CIL-Driving

Dynamic Conditional Imitation Learning for Autonomous Driving

https://github.com/hfailab/opencastkit

GraphCast: Learning skillful medium-range global weather forecasting

https://github.com/hill-a/stable-baselines

Generative Adversarial Imitation Learning

https://github.com/hkust-nlp/agentboard

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/hmi88/what

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/hoidn/ptychopinn

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/holarissun/prompt-oirl

Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond

https://github.com/horbachmp/TensorBandits

Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making

https://github.com/hosshonarvar/Image-Segmentation

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/howuhh/faster-trajectory-transformer

Offline Reinforcement Learning as One Big Sequence Modeling Problem

https://github.com/hsilva664/nstep_airl

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

https://github.com/httpwww404notfound/CRIR

Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning

https://github.com/huiwy/reflection-on-trees

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

https://github.com/huiyulhy/safe-control-gym

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

https://github.com/huofushuo/SID

Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding

https://github.com/hw9603/DQfD-PyTorch

Deep Q-learning from Demonstrations

https://github.com/hyunghona/emu

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/hyunin-lee/ForecasterSAC

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/iamollas/LionLearn/tree/master/LioNets_V2

LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information

https://github.com/ibm/auto-contrastive-generation

Jump to Conclusions: Short-Cutting Transformers With Linear Transformations

https://github.com/ibm/topography-searcher

Physics Inspired Approaches To Understanding Gaussian Processes

https://github.com/iclavera/cassie

Soft Actor-Critic Algorithms and Applications

https://github.com/idealab-isu/GPView

Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes

https://github.com/idsia/hhmarl_2d

Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering

https://github.com/idthanm/mpg

Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model

https://github.com/ikostrikov/jax-rl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/illidanlab/opolo-code

Generative Adversarial Imitation from Observation

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