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/kirthevasank/mps

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

https://github.com/kishorkuttan/Multi-Agent-Deep-Deterministic-Policy-Gradient-actor-critic-deep-reinforcement-for-capacity-manageme

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

https://github.com/kiwi1944/CRISense

Recurrent Models of Visual Attention

https://github.com/kjczarne/eager_gradcam_tf

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

https://github.com/kklipski/ALHE-projekt

Population Based Training of Neural Networks

https://github.com/kkonen/ros2learn

ROS2Learn: a reinforcement learning framework for ROS 2

https://github.com/kmdanielduan/DQN_Family_PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/kngwyu/intrinsic-rewards

Exploration by Random Network Distillation

https://github.com/kochlisGit/autonomous-vehicles-agent

Deep Reinforcement Learning with Double Q-learning

https://github.com/kollerlukas/safe-explorer

Safe Exploration in Continuous Action Spaces

https://github.com/komejisatori/ReinforcementCar

Playing Atari with Deep Reinforcement Learning

https://github.com/kondrasso/DQN

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

https://github.com/kongkip/MapsIRL-RHIP-JAX

Massively Scalable Inverse Reinforcement Learning in Google Maps

https://github.com/kosyoshida/simple-keras

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

https://github.com/koulanurag/opcc

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/kousun12/TFDE

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

https://github.com/kpertsch/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/kracr/document-level-relation-extraction

Multi-Hop Knowledge Graph Reasoning with Reward Shaping

https://github.com/krasing/DRLearningCollaboration

Continuous control with deep reinforcement learning

https://github.com/krasing/DRLearningContinuousControl

Continuous control with deep reinforcement learning

https://github.com/krasserm/fairseq-image-captioning

Self-critical Sequence Training for Image Captioning

https://github.com/krasserm/wdsr

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/krishanrana/multiplicative_controller_fusion

Multiplicative Controller Fusion: Leveraging Algorithmic Priors for Sample-efficient Reinforcement Learning and Safe Sim-To-Real Transfer

https://github.com/kristery/EthicsShaping

A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents

https://github.com/ksajan/DDPG-MAPE

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

https://github.com/kschweig/OfflineRL

A Dataset Perspective on Offline Reinforcement Learning

https://github.com/kshitij-ingale/Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ktkachuk/Atari-with-Q-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ku2482/discor.pytorch

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

https://github.com/ku2482/fqf-iqn-qrdqn.pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ku2482/gail-airl-ppo.pytorch

Proximal Policy Optimization Algorithms

https://github.com/ku2482/rljax

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ku2482/rltorch

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

https://github.com/ku2482/sac-discrete.pytorch

Prioritized Experience Replay

https://github.com/ku2482/slac.pytorch

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

https://github.com/ku2482/soft-actor-critic.pytorch

Prioritized Experience Replay

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