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/SinghJasdeep/Projecting-Conflicting-Gradients

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

https://github.com/SintolRTOS/multi-agent_Example

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

https://github.com/Skylerzhang023/MediaAISuperCloneBoy

Unity: A General Platform for Intelligent Agents

https://github.com/SmartSystems-UniAndes/ML_for_Solar_PV_Systems

A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn

https://github.com/SnDehghani/pcgrl_d_m

PCGRL: Procedural Content Generation via Reinforcement Learning

https://github.com/Sohojoe/ActiveRagdollAssaultCourse

Emergence of Locomotion Behaviours in Rich Environments

https://github.com/Sohojoe/ActiveRagdollStyleTransfer

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

https://github.com/Somya-Patra/Shell_ai

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

https://github.com/Sooram/GradCam-tensorflow

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

https://github.com/Sooram/cnn

Recurrent Models of Visual Attention

https://github.com/Sooram/stn

Recurrent Models of Visual Attention

https://github.com/Souphis/mobile_robot_rl

Continuous control with deep reinforcement learning

https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

A Distributional Perspective on Reinforcement Learning

https://github.com/StanfordVL/Gym

OpenAI Gym

https://github.com/StanfordVL/ac-teach

AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

https://github.com/StanfordVL/iGibsonChallenge2021

Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning

https://github.com/Steinheilig/Imbiss

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

https://github.com/Stephenfang51/Grad_CAM

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

https://github.com/Steve--Hunter/gym

OpenAI Gym

https://github.com/Steven-Ho/multiagent-particle-envs

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

https://github.com/Stevenhunter167/HER_Minigrid

Hindsight Experience Replay

https://github.com/Stippler/cow-simulator

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

https://github.com/Stomper10/CheXpert

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

https://github.com/StoneZhhh/cs682-NeuralDialogueSystems

End-to-End Task-Completion Neural Dialogue Systems

https://github.com/SudeepDasari/visual_foresight

Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

https://github.com/SulemanKhurram/ThesisExperiments

Weight Uncertainty in Neural Networks

https://github.com/SuperKam91/bnn

Weight Uncertainty in Neural Networks

https://github.com/Suryavf/SelfDrivingCar

Prioritized Experience Replay

https://github.com/SwapnilPande/MOReL

MOReL : Model-Based Offline Reinforcement Learning

https://github.com/SwarajPawar/Discrete-BCQ

Benchmarking Batch Deep Reinforcement Learning Algorithms

https://github.com/T3chy/DDPG

Continuous control with deep reinforcement learning

https://github.com/TaiseiAso/BiGruAttEncDec

A Simple, Fast Diverse Decoding Algorithm for Neural Generation

https://github.com/TakaOsa/LTD3

Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information

https://github.com/TalkToTheGAN/REGAN

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/TaoQin/taoqin.github.io

Suphx: Mastering Mahjong with Deep Reinforcement Learning

https://github.com/Tdjaaleb/Exploratory-Mean-Variance

Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework

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