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Deep Reinforcement Learning

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코드

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

https://github.com/gwthomas/gtml

Continuous control with deep reinforcement learning

https://github.com/gznyyb/deep_reinforcement_learning_Pong

Deep Reinforcement Learning with Double Q-learning

https://github.com/h-aboutalebi/SparceReward

Continuous control with deep reinforcement learning

https://github.com/haiguanl/DQN_GlobalRouting

A Deep Reinforcement Learning Approach for Global Routing

https://github.com/haje01/distper

Distributed Prioritized Experience Replay

https://github.com/halajadallah/RL-Quadcopter_project

Continuous control with deep reinforcement learning

https://github.com/hamidreza-mazandarani/SAMA-D3QL

A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications

https://github.com/hamishs/JAX-RL

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/hammer-wang/oml-ppo/blob/master/README.md

Automated Optical Multi-layer Design via Deep Reinforcement Learning

https://github.com/han-won/PlayingAtariWithMindSpore

Playing Atari with Deep Reinforcement Learning

https://github.com/haosulab/ManiSkill-Learn

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/hari-sikchi/loop

Learning Off-Policy with Online Planning

https://github.com/harvard-edge/source-seeking

Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller

https://github.com/harvitronix/reinforcement-learning-car

Playing Atari with Deep Reinforcement Learning

https://github.com/hbzhang/AwesomeSelfDriving

Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving

https://github.com/heidekrueger/bnelearn

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

https://github.com/heiner/scalable_agent

Learning to Navigate in Cities Without a Map

https://github.com/hemilpanchiwala/Dueling-Network-Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Dueling_Network_Architectures

Deep Reinforcement Learning with Double Q-learning

https://github.com/hemilpanchiwala/Hindsight-Experience-Replay

Continuous control with deep reinforcement learning

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

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

https://github.com/herbps10/adaptiveconformal

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

https://github.com/hex-plex/KiloBot-MultiAgent-RL

Guided Deep Reinforcement Learning for Swarm Systems

https://github.com/hhase/sacrum_dataset

Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

https://github.com/hhf1357924680/RL-FIN

Practical Deep Reinforcement Learning Approach for Stock Trading

https://github.com/hill-a/stable-baselines

Playing Atari with Deep Reinforcement Learning

https://github.com/hiwonjoon/nec

Neural Episodic Control

https://github.com/htdt/rainbow

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/huanzhang12/sa_ppo

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

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

ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate

https://github.com/huisun317/snn

Solving high-dimensional partial differential equations using deep learning

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

Asynchronous Methods for Deep Reinforcement Learning

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/hyecheol123/Summary_of_QT-Opt

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

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

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

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