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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/king-zark/self_critical

Self-critical Sequence Training for Image Captioning

https://github.com/kingsaint/InductiveExplainableLinkPrediction

Multi-Hop Knowledge Graph Reasoning with Reward Shaping

https://github.com/kini5gowda/ALBA-RL-for-VOS

ALBA : Reinforcement Learning for Video Object Segmentation

https://github.com/kirthevasank/mps

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

https://github.com/kishorkuttan/Multi-Agent-Deep-Deterministic-Policy-Gradient-actor-critic-deep-reinforcement-for-capacity-manageme

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

https://github.com/kiwi1944/CRISense

Dynamic Computational Time for Visual Attention

https://github.com/kjboggess/ijcai23

Toward Policy Explanations for Multi-Agent Reinforcement Learning

https://github.com/kkonen/ros2learn

ROS2Learn: a reinforcement learning framework for ROS 2

https://github.com/kmdanielduan/DQN_Family_PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/kngwyu/intrinsic-rewards

Exploration by Random Network Distillation

https://github.com/kochlisGit/autonomous-vehicles-agent

Deep Reinforcement Learning with Double Q-learning

https://github.com/kohakublueleaf/kgen

TIPO: Text to Image with Text Presampling for Prompt Optimization

https://github.com/kollerlukas/safe-explorer

Safe Exploration in Continuous Action Spaces

https://github.com/komejisatori/ReinforcementCar

Playing Atari with Deep Reinforcement Learning

https://github.com/kondrasso/DQN

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

https://github.com/koulanurag/opcc

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/kousun12/TFDE

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

https://github.com/kpertsch/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/kracr/document-level-relation-extraction

Multi-Hop Knowledge Graph Reasoning with Reward Shaping

https://github.com/krasing/DRLearningCollaboration

Continuous control with deep reinforcement learning

https://github.com/krasing/DRLearningContinuousControl

Continuous control with deep reinforcement learning

https://github.com/krasserm/fairseq-image-captioning

Self-critical Sequence Training for Image Captioning

https://github.com/krasserm/wdsr

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/kristery/EthicsShaping

A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents

https://github.com/ksajan/DDPG-MAPE

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

https://github.com/kschweig/OfflineRL

A Dataset Perspective on Offline Reinforcement Learning

https://github.com/kshitij-ingale/Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ktkachuk/Atari-with-Q-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ku2482/discor.pytorch

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

https://github.com/ku2482/fqf-iqn-qrdqn.pytorch

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ku2482/gail-airl-ppo.pytorch

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/ku2482/rljax

Distributional Reinforcement Learning with Quantile Regression

https://github.com/ku2482/rltorch

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

https://github.com/ku2482/sac-discrete.pytorch

Prioritized Experience Replay

https://github.com/ku2482/slac.pytorch

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/ku2482/soft-actor-critic.pytorch

Prioritized Experience Replay

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