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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/XiWen0627/MaxEnIRLinCycling

Maximum Entropy Deep Inverse Reinforcement Learning

https://github.com/XiangboGaoBarry/TexasHoldemAI

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/XingxingZhang/dress

Sentence Simplification with Deep Reinforcement Learning

https://github.com/Xingyu-Lin/mbpo_pytorch

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/XuehaiPan/Soft-Actor-Critic

Soft Actor-Critic Algorithms and Applications

https://github.com/XueyingBai/Model-Based-Reinforcement-Learning-for-Online-Recommendation

Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation

https://github.com/YHL04/agent57

Agent57: Outperforming the Atari Human Benchmark

https://github.com/YaCpotato/B4ResearchDeepaugment

Neural Architecture Search with Reinforcement Learning

https://github.com/YaCpotato/deepaugmentFix

Neural Architecture Search with Reinforcement Learning

https://github.com/YadiLao/MM-Tag

A Tree Search Algorithm for Sequence Labeling

https://github.com/YanSong97/Master-thesis

Noisy Networks for Exploration

https://github.com/YangRui2015/Modular_HER

Continuous control with deep reinforcement learning

https://github.com/Yangangren/Adversarial-Reinforcement-learning

Robust Adversarial Reinforcement Learning

https://github.com/Yangangren/Adversarial_RL

Robust Adversarial Reinforcement Learning

https://github.com/YanglanWang/classic_control

https://github.com/YaoMarkMu/DRQTRANS

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

https://github.com/Yihao-Sun/pytorch-mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/Yizhao111/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/YoUNG824/DDPG

Continuous control with deep reinforcement learning

https://github.com/YuansongFeng/MadMario

Deep Reinforcement Learning with Double Q-learning

https://github.com/YufanPaPa/GAN_SSN

Adversarial Learning for Neural Dialogue Generation

https://github.com/YuhangSong/Mega-Reward

Deep Reinforcement Learning that Matters

https://github.com/YushiChen/Auto-CNN-HSI-Classification

DARTS: Differentiable Architecture Search

https://github.com/Yuta123456/poker-ai

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/Yutongamber/MADDPG

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

https://github.com/ZD1wang/Traffiti

An Efficient Deep Reinforcement Learning Model for Urban Traffic Control

https://github.com/ZacharyChenpk/Local_ETHZ

Learning Dynamic Context Augmentation for Global Entity Linking

https://github.com/ZachisGit/LearningFromHumanPreferences

Deep reinforcement learning from human preferences

https://github.com/ZainRaza14/Neuro-Evolution

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

https://github.com/ZainRaza14/deepRL

Continuous control with deep reinforcement learning

https://github.com/Zartris/TD3_continuous_control

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/Zartris/TD3_multi_agent_tennis

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

https://github.com/Zeta36/chess-alpha-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/ZexinLi0w0/R3

Learning to Drive in a Day

https://github.com/Zhendong-Wang/arsm_image_captioning

Self-critical Sequence Training for Image Captioning

https://github.com/ZhengXinyue/fuck_papers

Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

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