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Deep Reinforcement Learning

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

https://github.com/RLAgent/state-marginal-matching

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

https://github.com/RLeike/connect-four

Playing Atari with Deep Reinforcement Learning

https://github.com/Rabrg/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/Rafael1s/Deep-Reinforcement-Learning-Udacity

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

https://github.com/RandyDeng/gym_connect4

Playing Atari with Deep Reinforcement Learning

https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection

Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing

https://github.com/Remtasya/DDPG-Actor-Critic-Reinforcement-Learning-Reacher-Environment

Continuous control with deep reinforcement learning

https://github.com/RevanMacQueen/LearningFromHumans

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

https://github.com/RexGLiu/rlpyt_crbp

Accelerated Methods for Deep Reinforcement Learning

https://github.com/RickyMexx/DeepRL-LTL

Modular Deep Reinforcement Learning with Temporal Logic Specifications

https://github.com/RickyMexx/DeepRL-LTLf

Modular Deep Reinforcement Learning with Temporal Logic Specifications

https://github.com/Ring367/A-Deep-Reinforcement-Learning-Network-for-Traffic-Light-Cycle-Control

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

https://github.com/Ring367/Double-Dueling-DQN-to-control-traffic-lights-on-SUMO

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

https://github.com/RingBDStack/DRL-DBSCAN/tree/main/DRL-DBSCAN-mindspore

Automating DBSCAN via Deep Reinforcement Learning

https://github.com/Roberto09/Dynamic-Attention-Model-for-VRP---Pytorch

A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems

https://github.com/RoblabWh/RobLearn

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

https://github.com/RobotMobile/rl-paper-review

Playing Atari with Deep Reinforcement Learning

https://github.com/RobvanGastel/meta-rl-algorithms

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/Roman-Kozachek/TradeBot

Deep Reinforcement Learning with Double Q-learning

https://github.com/SaminYeasar/off_policy_ac

Continuous control with deep reinforcement learning

https://github.com/SayhoKim/tetrisRL

Playing Atari with Deep Reinforcement Learning

https://github.com/SergiPonsa/Reinforcement-Learning-Sergi

Continuous control with deep reinforcement learning

https://github.com/SeungeonBaek/hybrid-agents-test

Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

https://github.com/ShashwatNigam99/MARBLER

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

https://github.com/ShawK91/Evolutionary-Reinforcement-Learning

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

https://github.com/Sheepsody/Batched-Impala-PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/Sheldonmao/Learning-To-walk

Continuous control with deep reinforcement learning

https://github.com/ShibiHe/Model-Free-Episodic-Control

Model-Free Episodic Control

https://github.com/ShibiHe/Q-Optimality-Tightening

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/ShivamShrirao/deep_Q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ShivamShrirao/deep_Q_learning_from_scratch

Playing Atari with Deep Reinforcement Learning

https://github.com/Shunichi09/PythonLinearNonlinearControl

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

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

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

https://github.com/SimarKareer/legged_gym

Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

https://github.com/SimonRamstedt/ddpg

Continuous control with deep reinforcement learning

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

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

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