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/xxkkrr/conv_rec_sys

Conversational Recommender System

https://github.com/xxlya/COS598D_Assignment1

Searching for A Robust Neural Architecture in Four GPU Hours

https://github.com/xyshadow/baseline_ddpg

Continuous control with deep reinforcement learning

https://github.com/xzb030/img_survey

Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approach

https://github.com/xzxzxzxz/interp

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

https://github.com/yadrimz/option-critic

The Option-Critic Architecture

https://github.com/yaleCat/Grad-CAM-pytorch

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

https://github.com/yamada-github-account/LearnBayesNN

Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

https://github.com/yangysc/resinet

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/yangyucheng000/University/tree/main/model-1/qdqbert

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

https://github.com/yangyucheng000/deepbsde

Solving high-dimensional partial differential equations using deep learning

https://github.com/yannbouteiller/gym-airsimdroneracinglab

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

https://github.com/yannbouteiller/rtgym

Reinforcement Learning with Random Delays

https://github.com/yanpei18345156216/BayesianOptimization

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

https://github.com/yaof20/real

Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization

https://github.com/yaoliucs/PQL

Provably Good Batch Reinforcement Learning Without Great Exploration

https://github.com/yaoyao-liu/mini-imagenet-tools

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

https://github.com/yaringal/ConcreteDropout

Concrete Dropout

https://github.com/yaringal/DropoutUncertaintyExps

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/yaringal/HeteroscedasticDropoutUncertainty

Deep Exploration via Bootstrapped DQN

https://github.com/yashchandak/SAS_RL

Reinforcement Learning When All Actions are Not Always Available

https://github.com/yashkant/PNAS-Binarized-Neural-Networks

Progressive Neural Architecture Search

https://github.com/yashpatel5400/clean_idp_rl

TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

https://github.com/yaxinchen666/dce_pricingRL

Playing Atari with Deep Reinforcement Learning

https://github.com/yenchehsiao/autonomousllmagentwithadaptingplanning

Reflexion: Language Agents with Verbal Reinforcement Learning

https://github.com/yhisaki/average-reward-drl

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

https://github.com/yifan-you-37/omnihang

OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation

https://github.com/yifan123/arxiv_spider

False Correlation Reduction for Offline Reinforcement Learning

https://github.com/yifan12wu/td3-jax

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/yifeizhou02/archer

CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

https://github.com/yihaosun1124/OfflineRL-Kit

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/yihenglin97/networked-marl

Multi-Agent Reinforcement Learning in Stochastic Networked Systems

https://github.com/yiliu1/Machine-Learning-in-Finance

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

https://github.com/yimingpeng/sac-master

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

https://github.com/yining043/SAC-discrete

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

https://github.com/yining043/TSP-improve

Learning Improvement Heuristics for Solving Routing Problems

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