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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/EyaRhouma/collaboration-competition-MADDPG

Continuous control with deep reinforcement learning

https://github.com/FJSam/SelfCritical_ImageCaptioning

Self-critical Sequence Training for Image Captioning

https://github.com/FaboNo/DRLND

Deep Reinforcement Learning with Double Q-learning

https://github.com/FauzaanQureshi/deep-Q-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/FenHua/SVA

Sparse Black-box Video Attack with Reinforcement Learning

https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation

Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification

https://github.com/FlixCoder/rust-es-optimizer

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/FlyienSHaDOw/continuous_control

Continuous control with deep reinforcement learning

https://github.com/FlyienSHaDOw/project_2_continuous_control

Continuous control with deep reinforcement learning

https://github.com/Frager/Deep-Neuroevolution-In-SC2LE

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/Francesco-Sovrano/Combining--experience-replay--with--exploration-by-random-network-distillation-

Exploration by Random Network Distillation

https://github.com/FrankPortman/stannis

Monte Carlo Q-learning for General Game Playing

https://github.com/Franklin-Zhang0/ReasonGen-R1

ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL

https://github.com/Fritschek/Reinforcement-Learning-for-Wireless

End-to-End Learning of Communications Systems Without a Channel Model

https://github.com/FudanNLP/Irl_gen

Toward Diverse Text Generation with Inverse Reinforcement Learning

https://github.com/GAMES-UChile/mogptk

SAMBA: Safe Model-Based & Active Reinforcement Learning

https://github.com/GaoFangshu/ELF-example

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

https://github.com/Gary-Shi/Tank

Playing Atari with Deep Reinforcement Learning

https://github.com/GauravPatel89/Car-Navigation-Simulation-using-TD3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/GauthierDmn/question_generation

Machine Comprehension by Text-to-Text Neural Question Generation

https://github.com/GeminiLight/virne

Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm

https://github.com/GeneralCoder365/agreemate

Decoupling Strategy and Generation in Negotiation Dialogues

https://github.com/GeorgeDUT/MetaRLSAS

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

https://github.com/GiuliaLanzillotta/INAS

Neural Architecture Search with Reinforcement Learning

https://github.com/Godofnothing/EfficientObjectDetection

Efficient Object Detection in Large Images using Deep Reinforcement Learning

https://github.com/GoingMyWay/dopamine_reward_decomposition

Prioritized Experience Replay

https://github.com/Gouet/DDPG_PendulumV1

Continuous control with deep reinforcement learning

https://github.com/GuanSuns/Simple-Human-in-the-Loop-ML-Interface

Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation

https://github.com/GuangpingYuan/PNN_Pong_A3C

Progressive Neural Networks

https://github.com/Guillaume-Cr/lunar_lander_per

Prioritized Experience Replay

https://github.com/GuillaumeDufau/Unsupervised_exploration

Planning to Explore via Self-Supervised World Models

https://github.com/GuyTevet/SeqGAN-eval

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/HJDQN/HJQ

Continuous control with deep reinforcement learning

https://github.com/HKUST-KnowComp/MnemonicReader

Reinforced Mnemonic Reader for Machine Reading Comprehension

https://github.com/Haichao-Zhang/IRL

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

https://github.com/HanbumKo/distributed-wqmix

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

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