Papers with Code 아카이브
Reinforcement Learning (RL)
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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Remtasya/DDPG-Actor-Critic-Reinforcement-Learning-Reacher-Environment
https://github.com/RevanMacQueen/LearningFromHumans
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
https://github.com/RexGLiu/rlpyt_crbp
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
https://github.com/Rintarooo/TSP_DRL_PointerNet
Neural Combinatorial Optimization with Reinforcement Learning
https://github.com/Rintarooo/TSP_DRL_PtrNet
Neural Combinatorial Optimization with Reinforcement Learning
https://github.com/Rintarooo/TSP_RL_Pointer_Actor_Critic
Neural Combinatorial Optimization with Reinforcement Learning
https://github.com/RobertKirk/roam-tools
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
https://github.com/RobvanGastel/meta-rl-algorithms
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
https://github.com/RojaImmanni/FlappyBird_using_RL
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques
https://github.com/Roman-Kozachek/TradeBot
https://github.com/Rowing0914/Atari-Grand-Challenge-Processor
https://github.com/Rowing0914/gym_modified
https://github.com/Runist/torch_maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/RunzheStat/TestMDP
https://github.com/Ryan-Ray-Martin/Market-Maker
https://github.com/RyanNavillus/Syllabus
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
https://github.com/RyanRizzo96/gym-FetchCustom
https://github.com/SAGNIKMJR/CODEBRIM_MetaQNN
https://github.com/SAGNIKMJR/MetaQNN_ImageClassification_PyTorch
Designing Neural Network Architectures using Reinforcement Learning
https://github.com/SAGNIKMJR/MetaQNN_ImageGenerationGAN_DiscriminatorFixed_PyTorch
Designing Neural Network Architectures using Reinforcement Learning
https://github.com/SAGNIKMJR/MetaQNN_ImageGenerationVCAE_PyTorch
Designing Neural Network Architectures using Reinforcement Learning
https://github.com/SJTU-VLG/ImageCaption-LBPF-qy
https://github.com/SagesAi/terminal-agent
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
https://github.com/SalvatoreCognetta/reasoning-agent-project
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
https://github.com/SaminYeasar/off_policy_ac
https://github.com/Sarah-wookey/RL_NetHack_2020
https://github.com/SaremS/WorldModel
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.