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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/StatisticalReinforcementLearningLab/oralytics_algorithm_design

공식 코드

Oralytics Reinforcement Learning Algorithm

https://github.com/StatisticalReinforcementLearningLab/oralytics_pilot_data

공식 코드

Oralytics Reinforcement Learning Algorithm

https://github.com/Stilwell-Git/Hindsight-Goal-Generation

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Exploration via Hindsight Goal Generation

https://github.com/SuReLI/llrl

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Lipschitz Lifelong Reinforcement Learning

https://github.com/SuerpX/Embedded-Self-Predictions

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Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions

https://github.com/SunwardTree/TDDPG-Rec

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A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation

https://github.com/Sweety-dm/RECALL

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Replay-enhanced Continual Reinforcement Learning

https://github.com/T3p/pois

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Policy Optimization via Importance Sampling

https://github.com/TIERS/NoisyKukaReacher

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Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

https://github.com/TILOS-AI-Institute/MacroPlacement

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Assessment of Reinforcement Learning for Macro Placement

https://github.com/TSummersLab/polgrad-multinoise

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Learning robust control for LQR systems with multiplicative noise via policy gradient

https://github.com/TSummersLab/robust-adaptive-control-multinoise

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Robust Learning-Based Control via Bootstrapped Multiplicative Noise

https://github.com/TZW1998/Taming-Stable-Diffusion-with-Human-Ranking-Feedback

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Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles

https://github.com/TakaOsa/adInfoHRL

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Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization

https://github.com/TakuyaHiraoka/Dropout-Q-Functions-for-Doubly-Efficient-Reinforcement-Learning

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Dropout Q-Functions for Doubly Efficient Reinforcement Learning

https://github.com/Tencent/TStarBots

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TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

https://github.com/ThibautTheate/An-Application-of-Deep-Reinforcement-Learning-to-Algorithmic-Trading

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An Application of Deep Reinforcement Learning to Algorithmic Trading

https://github.com/ThibautTheate/Unconstrained-Monotonic-Deep-Q-Network-algorithm

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Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks

https://github.com/Thinklab-SJTU/awesome-ml4co

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Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching

https://github.com/Thinksky5124/SVTAS

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End-to-End Streaming Video Temporal Action Segmentation with Reinforce Learning

https://github.com/TimSalimans/weight_norm

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

https://github.com/Tinker-Twins/MARL-Simulator

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Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement Learning

https://github.com/TofaraMoyo/Heirachical-Reward-Functions

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Creating Hierarchical Dispositions of Needs in an Agent

https://github.com/TonghanWang/EITI-EDTI

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Influence-Based Multi-Agent Exploration

https://github.com/TonghanWang/NDQ

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Learning Nearly Decomposable Value Functions Via Communication Minimization

https://github.com/ToniCreswell/Adversarial-Attack-On-Latent-Space

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LatentPoison - Adversarial Attacks On The Latent Space

https://github.com/TorchCraft/StarData

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STARDATA: A StarCraft AI Research Dataset

https://github.com/TorchCraft/TorchCraftAI

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Growing Action Spaces

https://github.com/Tushar-N/blockdrop

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BlockDrop: Dynamic Inference Paths in Residual Networks

https://github.com/UCLA-StarAI/Analogous-Disentangled-Actor-Critic

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Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration

https://github.com/UKPLab/emnlp2018-april

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APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

https://github.com/UKPLab/ijcai2019-relis

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Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

https://github.com/UKPLab/irj-neural-april

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Preference-based Interactive Multi-Document Summarisation

https://github.com/UKPLab/refresh2018-predicting-trends-from-arxiv

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Predicting Research Trends From Arxiv

https://github.com/UKPLab/tacl2020-interactive-ranking

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Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation

https://github.com/ULieu/RL_3well

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Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning

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