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Reinforcement Learning

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코드

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

https://github.com/katalinic/l2rl

Learning to reinforcement learn

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

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

https://github.com/kathyrnrouse/RL_CUIP

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

https://github.com/kavosh8/MAC

Mean Actor Critic

https://github.com/kay-ck/bsc-attack

PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning

https://github.com/kayuksel/pytorch-ars

Prioritized Experience Replay

https://github.com/kazuto1011/grad-cam-pytorch

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

https://github.com/kc-ml2/SimpleDreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/kc-ml2/darts

DARTS: Differentiable Architecture Search

https://github.com/kcorder/qmix_variants

The StarCraft Multi-Agent Challenge

https://github.com/kdexd/lang-emerge-parlai

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

https://github.com/keep9oing/DRQN-Pytorch-CartPole-v1

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/ken011528/UpsideDown-reinforcement-learning-implement-by-tensorflow

Training Agents using Upside-Down Reinforcement Learning

https://github.com/kenfuliang/Awesome-Reinforcement-Learning

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

https://github.com/kentsommer/pytorch-value-iteration-networks

Value Iteration Networks

https://github.com/kenya-sk/mc_dropout_tensorflow

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

https://github.com/keraJLi/rejax

Soft Actor-Critic for Discrete Action Settings

https://github.com/kered9/gym-microrts

Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS

https://github.com/kevin-yu-0602/NeuralUCB_257

Provably Efficient Reinforcement Learning with Linear Function Approximation

https://github.com/kevin5naug/summer_project

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/kevinduh/san_mrc

Stochastic Answer Networks for Machine Reading Comprehension

https://github.com/kevinzakka/recurrent-visual-attention

Recurrent Models of Visual Attention

https://github.com/kftam1994/Robo_Advisor

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/kh-mo/QA_wikisql

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/khanrc/pt.darts

DARTS: Differentiable Architecture Search

https://github.com/khashiii97/PNN

Progressive Neural Networks

https://github.com/khashiii97/Progressive-Neural-Networks-for-IDS

Progressive Neural Networks

https://github.com/kibeomKim/House3D_baseline

Building Generalizable Agents with a Realistic and Rich 3D Environment

https://github.com/kimbring2/DQFD_Minecraft

Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

https://github.com/kimhc6028/pathnet-pytorch

PathNet: Evolution Channels Gradient Descent in Super Neural Networks

https://github.com/kiminh/dual-policy-distillation

Dual Policy Distillation

https://github.com/kimsinjeong/keypt2subpx

Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task

https://github.com/kinalmehta/epymarl

The StarCraft Multi-Agent Challenge

https://github.com/king-zark/self_critical

Self-critical Sequence Training for Image Captioning

https://github.com/kingsaint/InductiveExplainableLinkPrediction

Multi-Hop Knowledge Graph Reasoning with Reward Shaping

https://github.com/kini5gowda/ALBA-RL-for-VOS

ALBA : Reinforcement Learning for Video Object Segmentation

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