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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
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/zhufq00/ddis-prediction

공식 코드

Learning to Describe for Predicting Zero-shot Drug-Drug Interactions

https://github.com/zhuhl98/acrab

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Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning

https://github.com/zhuliwen/pku_traffic_lights

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Learning to Share in Multi-Agent Reinforcement Learning

https://github.com/zhuliwen/roadnetsz

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MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

https://github.com/zhyang2226/ar-lopti

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Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs

https://github.com/zifuwanggg/jigsaw-r1

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Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles

https://github.com/zigned/nuplan_zigned

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Reinforced Imitative Trajectory Planning for Urban Automated Driving

https://github.com/zihangdai/ERAC-VAML

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From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction

https://github.com/zihaosheng/traffic-expertise-rl

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Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control

https://github.com/zipengxuc/ecs-visdial-rl

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Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue

https://github.com/zisikons/deep-rl

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Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

https://github.com/ziyizhang27/mvc-zigal

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Refining Few-Step Text-to-Multiview Diffusion via Reinforcement Learning

https://github.com/ziyuanma/dcc

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Learning Selective Communication for Multi-Agent Path Finding

https://github.com/ziyuguo99/image-generation-cot

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Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPO

https://github.com/zjlab-ammi/hs-omrl

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On Context Distribution Shift in Task Representation Learning for Offline Meta RL

https://github.com/zjlab-ammi/llm4teach

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Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents

https://github.com/zjunlp/knowrl

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KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

https://github.com/zjwzcx/GenNBV

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GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction

https://github.com/zlr20/saferl_kit

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SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving

https://github.com/zmsn-2077/cup-safe-rl

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Constrained Update Projection Approach to Safe Policy Optimization

https://github.com/znowu/mirror-learning

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Mirror Learning: A Unifying Framework of Policy Optimisation

https://github.com/zoharri/mbrl2

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Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation Approach

https://github.com/zpschang/MD3QN

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Distributional Reinforcement Learning for Multi-Dimensional Reward Functions

https://github.com/zrguo/observe-r1

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Observe-R1: Unlocking Reasoning Abilities of MLLMs with Dynamic Progressive Reinforcement Learning

https://github.com/zsoltzombori/plcop

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Prolog Technology Reinforcement Learning Prover

https://github.com/zuzuba/CISR_NeurIPS20

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Safe Reinforcement Learning via Curriculum Induction

https://github.com/zwhe99/deepmath

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DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning

https://github.com/zyh-pku/reinforcement-learning-and-market-making

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Reinforcement Learning in High-frequency Market Making

https://github.com/zyq-scut/rltf

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RLTF: Reinforcement Learning from Unit Test Feedback

https://github.com/zyr17/unilight

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Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

https://github.com/zzyunzhi/vds

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Automatic Curriculum Learning through Value Disagreement

https://github.com/zzzace2000/autodiagnosis

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Dynamic Measurement Scheduling for Event Forecasting using Deep RL

https://gitlab.ai.vub.ac.be/mreymond/morl-guide

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A Practical Guide to Multi-Objective Reinforcement Learning and Planning

https://gitlab.ai.vub.ac.be/yocoppen/svcn2

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Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning

https://gitlab.com/AAAL/DFRL

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Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

https://gitlab.com/AAAL/Fair-RL

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Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards

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