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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/hewlettpackard/dc-rl

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Sustainability of Data Center Digital Twins with Reinforcement Learning

https://github.com/hhase/sacrum_data-set

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Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

https://github.com/hhase/spinal-navigation-rl

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Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

https://github.com/hhexiy/opponent

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Opponent Modeling in Deep Reinforcement Learning

https://github.com/hhhusiyi-monash/UPDeT

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UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers

https://github.com/hhihn/HVCL

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Mixture-of-Variational-Experts for Continual Learning

https://github.com/hijkzzz/pymarl2

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Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning

https://github.com/hisaolabsustc/hyper-morl

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Learning Pareto Set for Multi-Objective Continuous Robot Control

https://github.com/hithmh/Actor-critic-with-stability-guarantee

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Actor-Critic Reinforcement Learning for Control with Stability Guarantee

https://github.com/hitsz-tmg/veripo

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VerIPO: Cultivating Long Reasoning in Video-LLMs via Verifier-Gudied Iterative Policy Optimization

https://github.com/hiwonjoon/ICML2019-TREX

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Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

https://github.com/hiyouga/easyr1

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SophiaVL-R1: Reinforcing MLLMs Reasoning with Thinking Reward

https://github.com/hjreachability/discriminating-hyperplane

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Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanes

https://github.com/hjyao00/r1-sharevl

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R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO

https://github.com/hkust-nlp/laser

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Learn to Reason Efficiently with Adaptive Length-based Reward Shaping

https://github.com/hltchkust/cqr4cqa

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Can Question Rewriting Help Conversational Question Answering?

https://github.com/hmhuy0/SIM-RL

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Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning

https://github.com/hmhyau/rl-intention

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What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes

https://github.com/hmishfaq/lmc-lsvi

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Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo

https://github.com/hmishfaq/lsac

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Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning

https://github.com/hn-rs/csa

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Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems

https://github.com/hnyu/seditor

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Towards Safe Reinforcement Learning with a Safety Editor Policy

https://github.com/holarissun/Data-Centric-OPE

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When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective

https://github.com/holarissun/npsco

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Novel Policy Seeking with Constrained Optimization

https://github.com/holmeswww/spring

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SPRING: Studying the Paper and Reasoning to Play Games

https://github.com/honghaow/FORK

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FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning

https://github.com/hongzimao/decima-sim

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Learning Scheduling Algorithms for Data Processing Clusters

https://github.com/hori-group/two-step-design

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Two-step reinforcement learning for model-free redesign of nonlinear optimal regulator

https://github.com/hossam-mossalam/multi-objective-deep-rl

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Multi-Objective Deep Reinforcement Learning

https://github.com/hs-kempten/lineflow

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LineFlow: A Framework to Learn Active Control of Production Lines

https://github.com/hse1223/distributional-ope-ebrm

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Distributional Off-policy Evaluation with Bellman Residual Minimization

https://github.com/hsinglukliu/puzzle-based_storage_appendix

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Reinforcement learning for multi-item retrieval in the puzzle-based storage system

https://github.com/hsvgbkhgbv/shapley-machine-naht

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Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc Teamwork

https://github.com/htdt/tempal

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Temporal Alignment for History Representation in Reinforcement Learning

https://github.com/hu-po/pySACQ

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Learning by Playing - Solving Sparse Reward Tasks from Scratch

https://github.com/hu-zijing/b2-diffurl

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Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards

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