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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/DartML/PPO-Stein-Control-Variate

Action-depedent Control Variates for Policy Optimization via Stein's Identity

https://github.com/Dascka/dl_q-learning_comparator

Deep Reinforcement Learning that Matters

https://github.com/DataCanvasIO/Hypernets

Neural Architecture Search with Reinforcement Learning

https://github.com/DataScienceNigeria/D-Kitty-D-Claw.-Robotics-Benchmarks-for-Learning-with-Low-Cost-Robots-

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

https://github.com/DataXujing/PNASNet_pytorch

Progressive Neural Architecture Search

https://github.com/DavidJanz/successor_uncertainties_atari

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

https://github.com/DavidJanz/successor_uncertainties_tabular

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

https://github.com/Deci-AI/super-gradients

Progressive Neural Architecture Search

https://github.com/DeepLearningCUCS/RLVRP

Reinforcement Learning for Solving the Vehicle Routing Problem

https://github.com/Deepest-Project/WorldModels-A3C

https://github.com/Dekki-Aero/DDPG

Continuous control with deep reinforcement learning

https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Denbergvanthijs/imbDRL

Deep Reinforcement Learning with Double Q-learning

https://github.com/DerrickGXD/unsupervised-keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DevSlem/OfflineRL/blob/main/chap1_introduction.ipynb

Off-Policy Actor-Critic

https://github.com/DianYu420376/multi-agent-RL-numerical-simulation

Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach

https://github.com/Dikshuy/SMARTS-lite

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

https://github.com/Div99/IQ-Learn

Generative Adversarial Imitation Learning

https://github.com/Dlut-lab-zmn/Image-Captioning-Attack

Self-critical Sequence Training for Image Captioning

https://github.com/DoctorLiQ/AutoML

DARTS: Differentiable Architecture Search

https://github.com/DrArryYao/RL-SC

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/DrJupiter/Deep-Reinforcement-Rubiks-Cube-Solver

Revisiting Fundamentals of Experience Replay

https://github.com/DuaneNielsen/keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DuaneNielsen/rnd

Exploration by Random Network Distillation

https://github.com/Duckkkky/smarts

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

https://github.com/Dungyichao/Electric-Vehicle-Route-Planning-on-Google-Map-Reinforcement-Learning

Value Iteration Networks

https://github.com/EACN2022/EACN

Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Weight Uncertainty in Neural Networks

https://github.com/Eclectic-Sheep/sheeprl

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/EdNiehues/simulacao-de-projeto-para-mapeamento

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/ElisevanderPol/symmetrizer

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

https://github.com/ElleryL/gradient_estimator

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

https://github.com/EmbodiedVision/explorethecontext

Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models

https://github.com/EndingCredits/gym

https://github.com/EpiSci/SoCRATES

A Scalable and Reproducible System-on-Chip Simulation for Reinforcement Learning

https://github.com/Epsilon10/MARL

Soft Actor-Critic Algorithms and Applications

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