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Reinforcement Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/KevinEloff/rl-collaborative-communication

공식 코드

Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language

https://github.com/Khrylx/EgoPose

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Ego-Pose Estimation and Forecasting as Real-Time PD Control

https://github.com/KohlerHECTOR/DPDTreeEstimator

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Interpretable Decision Tree Search as a Markov Decision Process

https://github.com/KohlerHECTOR/interpreter-py

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Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning

https://github.com/KuangenZhang/ATD3

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Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics

https://github.com/KunalP117/YouTube_project

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Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

https://github.com/KyunghyunLee/aes-rl

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An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search

https://github.com/LAIRLAB/ARS-experiments

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Exploration in Action Space

https://github.com/LAIRLAB/contrasting_exploration_rl

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Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective

https://github.com/LCAS/deep_rl_for_swarms

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Deep Reinforcement Learning for Swarm Systems

https://github.com/LTL2Action/LTL2Action

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LTL2Action: Generalizing LTL Instructions for Multi-Task RL

https://github.com/LantaoYu/SeqGAN

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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/LeCAR-Lab/SafeDPA

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Safe Deep Policy Adaptation

https://github.com/LeaMarion/quantum-enhanceable-deep-rl

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Quantum enhancements for deep reinforcement learning in large spaces

https://github.com/Learning4Optimization-HUST/H-TSP

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H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem

https://github.com/Learning4Optimization-HUST/Pointerformer

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Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem

https://github.com/LearningToGatherWithoutCommunication/Ring

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Learning to Gather without Communication

https://github.com/Lei-Kun/Uni-O4

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Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization

https://github.com/Lhongpei/ipps-drl

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Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning

https://github.com/LiQisResearch/KDD--RF-MA3S

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Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning

https://github.com/Limmen/gym-idsgame

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Finding Effective Security Strategies through Reinforcement Learning and Self-Play

https://github.com/Limmen/gym-optimal-intrusion-response

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Learning Intrusion Prevention Policies through Optimal Stopping

https://github.com/LinZichuan/AdMRL

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Model-based Adversarial Meta-Reinforcement Learning

https://github.com/LinghengMeng/LSTM-TD3

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Memory-based Deep Reinforcement Learning for POMDPs

https://github.com/LittleYUYU/CoaCor

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CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

https://github.com/LittleYUYU/Interactive-Semantic-Parsing

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Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

https://github.com/LorenJAnderson/uav-2d-greedyshooter-rl

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On Solving the 2-Dimensional Greedy Shooter Problem for UAVs

https://github.com/LorenzoFederici/RobustTrajectoryDesignbyRL

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Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions

https://github.com/LostXine/elo-sac

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Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?

https://github.com/Lukeeeeee/baconian-project

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Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning

https://github.com/LunjunZhang/world-model-as-a-graph

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World Model as a Graph: Learning Latent Landmarks for Planning

https://github.com/MAS-anony/ASN

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Action Semantics Network: Considering the Effects of Actions in Multiagent Systems

https://github.com/MIRALab-USTC/RL-POMBU

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Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization

https://github.com/MJinXiang/Reasoning-Table

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Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning

https://github.com/MLE-Dojo/MLE-Dojo

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MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering

https://github.com/MLIUB/PURE

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation

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