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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/gair-nlp/deepresearcher

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DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments

https://github.com/gair-nlp/maye

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Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme

https://github.com/gair-nlp/octothinker

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OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling

https://github.com/galatolofederico/a2c-te-nog

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Solving the scalarization issues of Advantage-based Reinforcement Learning Algorithms

https://github.com/ganjiro/offlinemania

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A Benchmark Environment for Offline Reinforcement Learning in Racing Games

https://github.com/gaoly/mopn

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Multi-objective Pointer Network for Combinatorial Optimization

https://github.com/gaoqitong/gradient-importance-learning

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Gradient Importance Learning for Incomplete Observations

https://github.com/gaoqitong/vlbm

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Variational Latent Branching Model for Off-Policy Evaluation

https://github.com/gaosh/bgpo

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Bregman Gradient Policy Optimization

https://github.com/gaoyuankidult/PepperSocial

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Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning

https://github.com/gasolsun36/dynamicrag

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DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation

https://github.com/gavlegoat/safe-learning

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Neurosymbolic Reinforcement Learning with Formally Verified Exploration

https://github.com/gd-goblin/NTU_DB_Data_Loader

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C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/geaps/geaps

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Goal Exploration Augmentation via Pre-trained Skills for Sparse-Reward Long-Horizon Goal-Conditioned Reinforcement Learning

https://github.com/gebob19/natural-policy-gradient-reinforcement-learning

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Understanding the Effects of Second-Order Approximations in Natural Policy Gradient Reinforcement Learning

https://github.com/geek-ai/MAgent

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MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

https://github.com/gemcollector/TLDA

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Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning

https://github.com/gen-verse/mmada

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MMaDA: Multimodal Large Diffusion Language Models

https://github.com/georgeliu233/scene-rep-transformer

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Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving

https://github.com/georgewanglz2019/LLMTraveler

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AI-Driven Day-to-Day Route Choice

https://github.com/geraudnt/skill_machines

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Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning

https://github.com/geshang777/Seg-R1

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Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning

https://github.com/gfnorg/gfn-diffusion

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From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training

https://github.com/gfnorg/gfn_vs_hvi

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GFlowNets and variational inference

https://github.com/gfonseca92/bandits-lib

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Improving Portfolio Optimization Results with Bandit Networks

https://github.com/ghimiremukesh/autonomous-driving

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Lane Change Decision-Making through Deep Reinforcement Learning

https://github.com/giacomoni/raynet

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RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols

https://github.com/gianluigigrandesso/cacto

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CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory Optimization

https://github.com/giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms

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Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks

https://github.com/giarcieri/robust-optimal-maintenance-planning-through-reinforcement-learning-and-rllib

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POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance

https://github.com/gilesluo/dtr-bench

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DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime

https://github.com/gilesluo/reassessdtr

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Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination

https://github.com/gilgameshd/grader

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Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

https://github.com/giorgioangel/dsym

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Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning

https://github.com/giorgiograni/jssp_actor-critic_agasucci_monaci_grani

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An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents

https://github.com/giseung-park/blockseq

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Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning

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