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Reinforcement Learning (RL)

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하위 태스크: 6
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보존된 평가 기록: 3

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/Eversee22/Policy-GNN

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Policy-GNN: Aggregation Optimization for Graph Neural Networks

https://github.com/FMZennaro/CTF-RL

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Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge

https://github.com/FMZennaro/CTF-SQL

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Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning Agents

https://github.com/FOCAL-ICLR/FOCAL-ICLR

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FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization

https://github.com/FXDevailly/IG-RL

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IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

https://github.com/Facebear-ljx/DOGE

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When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning

https://github.com/Facebear-ljx/PROTO

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PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning

https://github.com/Facebear-ljx/RGM

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Mind the Gap: Offline Policy Optimization for Imperfect Rewards

https://github.com/Fang-Lin93/DAC

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Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning

https://github.com/FanmingL/Recurrent-Offpolicy-RL

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Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate

https://github.com/Farama-Foundation/PettingZoo

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PettingZoo: Gym for Multi-Agent Reinforcement Learning

https://github.com/FarnazAdib/Crash_course_on_RL

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A Crash Course on Reinforcement Learning

https://github.com/Feicheng-Wang/LQAC_code

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Exact Asymptotics for Linear Quadratic Adaptive Control

https://github.com/FilippoAiraldi/learning-safety-in-mpc-based-rl

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Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes

https://github.com/FlemmingKondrup/DeepVent

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Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

https://github.com/FlorenceFeng/ENIAC

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Provably Correct Optimization and Exploration with Non-linear Policies

https://github.com/FlorenceFeng/StateDecoding

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Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning

https://github.com/FortsAndMills/Learning-Reinforcement-Learning

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Modern Deep Reinforcement Learning Algorithms

https://github.com/Francesco-Sovrano/Partitioned-A3C-for-RogueInABox

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Crawling in Rogue's dungeons with (partitioned) A3C

https://github.com/FredericLoge/T1DM_qlearning

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Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement Learning

https://github.com/Future-Power-Networks/MAPDN

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Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks

https://github.com/GIOVRUSSO/Control-Group-Code

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Driving Reinforcement Learning with Models

https://github.com/GMC-DRL/MetaBox

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Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study

https://github.com/GauravPandeyLab/lens-learning-ensembles-using-reinforcement-learning

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Developing parsimonious ensembles using predictor diversity within a reinforcement learning framework

https://github.com/GeminiLight/flag-vne

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FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

https://github.com/GhadaSokar/Dynamic-Sparse-Training-for-Deep-Reinforcement-Learning

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Dynamic Sparse Training for Deep Reinforcement Learning

https://github.com/GhanshyamVerma/Explainable-Recommender-System

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Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning

https://github.com/Ghiara/HELIOS

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Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement Learning

https://github.com/GonQue/gbac

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Learning to Perceive in Deep Model-Free Reinforcement Learning

https://github.com/Gongzhu-Society/MrIfOffline

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ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement Learning

https://github.com/GraphNAS/GraphNAS-simple

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GraphNAS: Graph Neural Architecture Search with Reinforcement Learning

https://github.com/Grey-z/FairAgent

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Enhancing New-item Fairness in Dynamic Recommender Systems

https://github.com/Grottoh/Deep-Active-Inference-for-Partially-Observable-MDPs

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Deep Active Inference for Partially Observable MDPs

https://github.com/GuanSuns/ASGRL

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Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity

https://github.com/GuoLanqing/ReLLIE

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ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement

https://github.com/GuyTevet/CLoSD

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CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control

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