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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/MarcusOlivecrona/REINVENT

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Molecular De Novo Design through Deep Reinforcement Learning

https://github.com/MarkusRabe/cadet

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Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

https://github.com/MartinBertran/InstanceAgnosticPolicyEnsembles

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Instance based Generalization in Reinforcement Learning

https://github.com/MarvinLer/pypownet

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Design and implementation of an environment for Learning to Run a Power Network (L2RPN)

https://github.com/MasaKat0/OPE_CS

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Off-Policy Evaluation and Learning for External Validity under a Covariate Shift

https://github.com/MatNLP/KEHRL

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KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning

https://github.com/MathPhysSim/FERMI_RL_Paper

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Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL

https://github.com/MatheusMRFM/STIM

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Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning

https://github.com/MatiasAlvo/Neural_inventory_control

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Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization

https://github.com/MatthewJA/Inverse-Reinforcement-Learning

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Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences

https://github.com/MaxSobolMark/OOO

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Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias

https://github.com/McGill-NLP/polytropon

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Combining Modular Skills in Multitask Learning

https://github.com/MehdiKhamassi/SocialMetaLearning

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Active exploration in parameterized reinforcement learning

https://github.com/Mehooz/BIRD_code

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Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

https://github.com/MeixinZhu/Velocity_control

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Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving

https://github.com/MianchuWang/LOM

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Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement Learning

https://github.com/MichaelAllen1966/learninghospital

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Integrating Deep Reinforcement Learning Networks with Health System Simulations

https://github.com/Miffyli/rl-action-space-shaping

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Action Space Shaping in Deep Reinforcement Learning

https://github.com/Miffyli/rl-human-prior-tricks

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Distilling Reinforcement Learning Tricks for Video Games

https://github.com/MikeTimmerman-ae/RL_Drone_Control

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Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

https://github.com/MikulasZelinka/pyfiction

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Using reinforcement learning to learn how to play text-based games

https://github.com/MishaLaskin/curl

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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/MiuLab/DDQ

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Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

https://github.com/MiuLab/KB-InfoBot

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Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

https://github.com/MiuLab/MUSE

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MUSE: Modularizing Unsupervised Sense Embeddings

https://github.com/MiuLab/UserSimulator

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A User Simulator for Task-Completion Dialogues

https://github.com/Mohammadamin-Barekatain/multipolar

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MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics

https://github.com/MoniFarsang/ppo-clipping-approaches

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Decaying Clipping Range in Proximal Policy Optimization

https://github.com/MouseHu/GEM

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Generalizable Episodic Memory for Deep Reinforcement Learning

https://github.com/MrtnMndt/meta-learning-CODEBRIM

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Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset

https://github.com/MuhangTian/Fair-MORL-AAMAS

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Welfare and Fairness in Multi-objective Reinforcement Learning

https://github.com/Multi-Agent-Networks/NaRLA

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Giving Up Control: Neurons as Reinforcement Learning Agents

https://github.com/MushroomRL/mushroom-rl

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MushroomRL: Simplifying Reinforcement Learning Research

https://github.com/MyCaffe/MyCaffe

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MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

https://github.com/NMerlis/TabulaRL

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Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies

https://github.com/NPLawrence/RL-MPC

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A view on learning robust goal-conditioned value functions: Interplay between RL and MPC

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