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/heegyukim/open-korean-instructions

LIMA: Less Is More for Alignment

https://github.com/hegde95/ViZDoom_with_Sound

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/heidekrueger/bnelearn

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

https://github.com/helpstonex/crazyara

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

https://github.com/hemilpanchiwala/Dueling-Network-Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Dueling_Network_Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Hindsight-Experience-Replay

Continuous control with deep reinforcement learning

https://github.com/hengyuan-hu/instruct-rl

Language Instructed Reinforcement Learning for Human-AI Coordination

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

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

https://github.com/herbps10/adaptiveconformal

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

https://github.com/hex-plex/KiloBot-MultiAgent-RL

Guided Deep Reinforcement Learning for Swarm Systems

https://github.com/heysoos/CriticalForagingOrgs

Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents

https://github.com/heysoos/critical-ising-evolution

Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents

https://github.com/hfahrudin/reptile_implement_tf2

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

https://github.com/hhase/sacrum_dataset

Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

https://github.com/hhf1357924680/RL-FIN

Practical Deep Reinforcement Learning Approach for Stock Trading

https://github.com/hietalajulius/clothmanip

Hindsight Experience Replay

https://github.com/hietalajulius/dynamic-cloth-folding

Hindsight Experience Replay

https://github.com/higcompetition/tournament

OpenSpiel: A Framework for Reinforcement Learning in Games

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

Playing Atari with Deep Reinforcement Learning

https://github.com/hitcszq/PRRLRE

Posterior-regularized REINFORCE for Instance Selection in Distant Supervision

https://github.com/hiwonjoon/nec

Neural Episodic Control

https://github.com/hjjimmykim/SchwabRoyale

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

https://github.com/hjjpku/dynamic_graph_caption

Self-critical Sequence Training for Image Captioning

https://github.com/hlr/vln-trans

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

https://github.com/hmhuy0/SIM-RL

Proximal Policy Optimization Algorithms

https://github.com/hmomin/CORL-compare

CORL: Research-oriented Deep Offline Reinforcement Learning Library

https://github.com/holarissun/rewardmodelingbeyondbradleyterry

Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs

https://github.com/holman57/ML-Education

Learning Curriculum Policies for Reinforcement Learning

https://github.com/holounic/DvD-TD3

Effective Diversity in Population Based Reinforcement Learning

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

Offline Reinforcement Learning as One Big Sequence Modeling Problem

https://github.com/howuhh/sac-n-jax

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble

https://github.com/hoyeoplee/pytorch-maml

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

https://github.com/hrdwsong/BayesianCNN-Paddle

Weight Uncertainty in Neural Networks

https://github.com/hs2k/pytorch-smoothgrad

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

https://github.com/hsgrandhi/AIProject

World Models

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