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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/SNAS-Series/SNAS-Series

공식 코드

SNAS: Stochastic Neural Architecture Search

https://github.com/SPEAR-IIT/CQSim

공식 코드

Deep Reinforcement Agent for Scheduling in HPC

https://github.com/SRI-AIC/InterestingnessXRL

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Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations

https://github.com/SaeedTafazzol/curious_explorer

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Curious Exploration and Return-based Memory Restoration for Deep Reinforcement Learning

https://github.com/SafeRL-Lab/Safe-Multi-Objective-MuJoCo

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Safe and Balanced: A Framework for Constrained Multi-Objective Reinforcement Learning

https://github.com/SaminYeasar/Off_Policy_Adversarial_Inverse_Reinforcement_Learning

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Off-Policy Adversarial Inverse Reinforcement Learning

https://github.com/SaminYeasar/sparse_reg

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Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity

https://github.com/SarodYatawatta/smart-calibration

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Deep reinforcement learning for smart calibration of radio telescopes

https://github.com/Scitator/Run-Skeleton-Run

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Run, skeleton, run: skeletal model in a physics-based simulation

https://github.com/Scitator/run-skeleton-run-in-3d

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Sample Efficient Ensemble Learning with Catalyst.RL

https://github.com/ScottJordan/EvaluationOfRLAlgs

공식 코드

Evaluating the Performance of Reinforcement Learning Algorithms

https://github.com/SeldonIO/alibi

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Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning

https://github.com/SenneDeproost/CM-GP

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Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution

https://github.com/ShangtongZhang/DeepRL

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QUOTA: The Quantile Option Architecture for Reinforcement Learning

https://github.com/ShaniGam/RL-GAN

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Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

https://github.com/ShawK91/erl_paper_nips18

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Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/Shawn-Guo-CN/EmergentNumerals

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Emergence of Numeric Concepts in Multi-Agent Autonomous Communication

https://github.com/Shen-Lab/Drug-Combo-Generator

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Network-principled deep generative models for designing drug combinations as graph sets

https://github.com/Sheng-J/DOM-Q-NET

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DOM-Q-NET: Grounded RL on Structured Language

https://github.com/ShengrenHou/Energy-management-MIP-Deep-Reinforcement-Learning

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Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm

https://github.com/Shilun-Allan-Li/2048-reinforcement_learning

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Playing 2048 With Reinforcement Learning

https://github.com/Shuijing725/CrowdNav_DSRNN

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Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning

https://github.com/Shuijing725/VAE_trait_inference

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Learning to Navigate Intersections with Unsupervised Driver Trait Inference

https://github.com/Skylark0924/Gamma_Reward

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Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal Control

https://github.com/Skylark0924/TendonTrack_PILCO

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Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge

https://github.com/SkyworkAI/Skywork-R1V

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Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning

https://github.com/SmartData-Polito/RL-IoT

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RL-IoT: Reinforcement Learning to Interact with IoT Devices

https://github.com/Sohojoe/ppo-dash

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PPO Dash: Improving Generalization in Deep Reinforcement Learning

https://github.com/Sonkyunghwan/QTRAN

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QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

https://github.com/SonyAI/PandemicSimulator

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Reinforcement Learning for Optimization of COVID-19 Mitigation policies

https://github.com/Space-Robotics-Laboratory/rlstar

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RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots

https://github.com/StanfordAI4HI/CSRL

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Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

https://github.com/StanfordASL/BaRC

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BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning

https://github.com/StanfordASL/RSIRL

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Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods

https://github.com/StatNLP/mt-correct-mark-interface

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Correct Me If You Can: Learning from Error Corrections and Markings

https://github.com/StatisticalReinforcementLearningLab/DyadicRLCode

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Dyadic Reinforcement Learning

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