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/franroldans/custom_gym

OpenAI Gym

https://github.com/freedombenLiu/ParlAI

ParlAI: A Dialog Research Software Platform

https://github.com/frgfm/Holocron

TAdam: A Robust Stochastic Gradient Optimizer

https://github.com/frgfm/torch-cam

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

https://github.com/fshamshirdar/pytorch-rdpg

Continuous control with deep reinforcement learning

https://github.com/fusion-ml/trajectory-information-rl

An Experimental Design Perspective on Model-Based Reinforcement Learning

https://github.com/gabrielsantosrv/coco-caption

Analysis of diversity-accuracy tradeoff in image captioning

https://github.com/gaetanserre/lipo

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/gaetanserre/stochastic-global-optimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/gair-nlp/torl

ToRL: Scaling Tool-Integrated RL

https://github.com/gammatauai/leetcode-hard-gym

Reflexion: Language Agents with Verbal Reinforcement Learning

https://github.com/garyzyr001/rethinking-airl

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

https://github.com/gauthambekal93/hvac_continual_rl

Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning

https://github.com/gazealytics/gazealytics-master

Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning

https://github.com/gbarello/CPCLibriSpeech

Representation Learning with Contrastive Predictive Coding

https://github.com/gbotev/PGPortfolio

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/gcomb/GCOMB

Learning Heuristics over Large Graphs via Deep Reinforcement Learning

https://github.com/gcp/leela-zero

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

https://github.com/gcucurull/maml_flax

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

https://github.com/gd-goblin/C-3PO_Motion_Retargeting_Module

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/gd-goblin/C-3PO_RobotControlModule

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/gd-zhang/noisy-K-FAC

Noisy Natural Gradient as Variational Inference

https://github.com/gebob19/REPTILE-Metalearning

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

https://github.com/gebob19/cscd94-metalearning

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

https://github.com/gebob19/cscd94_metalearning

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

https://github.com/geekJZY/arcticnet

SkipNet: Learning Dynamic Routing in Convolutional Networks

https://github.com/geeky-wizard/Atari-Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/gen-verse/reasonflux

Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning

https://github.com/gengsinong/samq

Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions

https://github.com/geohot/twitchcoq

Value Prediction Network

https://github.com/georgeliu233/scene-rep-transformer

Model-free Deep Reinforcement Learning for Urban Autonomous Driving

https://github.com/georgesung/TD3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/georgkruse/cleanqrl

Continuous control with deep reinforcement learning

https://github.com/geox-lab/cmn

Progressive Neural Networks

https://github.com/geraudnt/boolean_composition

A Boolean Task Algebra for Reinforcement Learning

https://github.com/gg-Dema/VBLRL

Model-based Lifelong Reinforcement Learning with Bayesian Exploration

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