Papers with Code 아카이브

Reinforcement Learning

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

https://github.com/hsilva664/nstep_airl

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

https://github.com/htdt/rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/huangjiancong1/rlkit_autotune

Hyperparameter Auto-tuning in Self-Supervised Robotic Learning

https://github.com/huanzhang12/sa_ppo

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

https://github.com/huaweicloud/vm-placement-dataset

VMAgent: Scheduling Simulator for Reinforcement Learning

https://github.com/huggingface/pytorch-image-models

LaProp: Separating Momentum and Adaptivity in Adam

https://github.com/huggingface/trl

Fine-Tuning Language Models from Human Preferences

https://github.com/hughsalimbeni/bayesian_benchmarks

Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors

https://github.com/huihanl/rl_unplugged

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

https://github.com/huisun317/snn

Solving high-dimensional partial differential equations using deep learning

https://github.com/huiwenzhang/oc-tf

The Option-Critic Architecture

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

Safe Exploration in Continuous Action Spaces

https://github.com/hulanwin/A3C-DRL

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/humanmllm/r1-omni

R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning

https://github.com/husthuaan/AAT

Self-critical Sequence Training for Image Captioning

https://github.com/husthuaan/AoANet

Self-critical Sequence Training for Image Captioning

https://github.com/huyng/incertae

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

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

Deep Q-learning from Demonstrations

https://github.com/hyang0129/foodclassapp

Regularizing Neural Networks by Penalizing Confident Output Distributions

https://github.com/hydesmondliu/b2rl

B2RL: An open-source Dataset for Building Batch Reinforcement Learning

https://github.com/hydra-hoard/hydra

Hydra: A Peer to Peer Distributed Training & Data Collection Framework

https://github.com/hyecheol123/Summary_of_QT-Opt

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

https://github.com/hyeonahkimm/genetic_gfn

De novo Drug Design using Reinforcement Learning with Multiple GPT Agents

https://github.com/hyerinshelly/RL_COVID-19_Korea

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

https://github.com/hzwer/LearningToPaint

Learning to Paint With Model-based Deep Reinforcement Learning

https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient

Continuous control with deep reinforcement learning

https://github.com/iDataist/Navigation-with-Deep-Q-Network

Prioritized Experience Replay

https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement

Continuous control with deep reinforcement learning

https://github.com/iSarmad/RL-GAN-Net

RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion

https://github.com/ianchar/gpide

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks

https://github.com/ianlimle/ItsMeMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/iassael/learning-to-communicate

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

https://github.com/iassael/tax-evasion-torch

Using deep Q-learning to understand the tax evasion behavior of risk-averse firms

https://github.com/ibrahimSouleiman/GuessWhat

End-to-end optimization of goal-driven and visually grounded dialogue systems

https://github.com/icaros-usc/evostone2

Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space

https://github.com/iclavera/cassie

Soft Actor-Critic Algorithms and Applications

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