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/ku2482/wappo.pytorch

Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion

https://github.com/kuanzi/Bayesian_quant

Model compression via distillation and quantization

https://github.com/kubeflow/katib

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

https://github.com/kumaa001/Tractable_RL_for_STL_Objectives

Tractable Reinforcement Learning of Signal Temporal Logic Objectives

https://github.com/kumar-shridhar/BayesianConvNet

Weight Uncertainty in Neural Networks

https://github.com/kumar-shridhar/PyTorch-BayesianCNN

Weight Uncertainty in Neural Networks

https://github.com/kunalp117/youtube-commenting-analysis

Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

https://github.com/kushagra06/DDPG

Continuous control with deep reinforcement learning

https://github.com/kushagra06/SAC

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

https://github.com/ky941122/darts-experiment

DARTS: Differentiable Architecture Search

https://github.com/l1t1/lz19

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

https://github.com/la3lma/Chez

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/la3lma/chezjulia

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Playing Atari with Deep Reinforcement Learning

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

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

https://github.com/laiviet/maml

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

https://github.com/laiyao1/maskplace

MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning

https://github.com/lamantinushka/StructuredCovariance

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

https://github.com/lambertinialessandro/RL-FinalProject

What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes

https://github.com/langorou/vamperouge

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

https://github.com/lanqingli1993/focal-iclr

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

https://github.com/lasseuth1/blood_bowl2

Automated Curriculum Learning by Rewarding Temporally Rare Events

https://github.com/lastmile-ai/aiconfig

Optimizing Prompts for Text-to-Image Generation

https://github.com/laurendelong21/mars

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

https://github.com/lava-lab/safe-slac

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/lbenbaccar/Deep-Reinforcement-Learning-with-Double-Q-Learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/lduml/blog

Stochastic Answer Networks for Machine Reading Comprehension

https://github.com/learn-to-race/l2r

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

https://github.com/learnermaxRL/PPO_A2C

Efficient Parallel Methods for Deep Reinforcement Learning

https://github.com/learningandplanningICLR/learningandplanning

Uncertainty-sensitive Learning and Planning with Ensembles

https://github.com/lecholin/DynamicLSTM

Long Short-Term Memory with Dynamic Skip Connections

https://github.com/lee-seon-woo/tscp2_pytorch

Representation Learning with Contrastive Predictive Coding

https://github.com/leehe228/LogisticsEnv

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

https://github.com/leehoon7/flow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

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

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

https://github.com/leelachesszero/lczero

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

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