https://github.com/BharathMuppasani/NeurIPS-Submission-2024
공식 코드Towards Effective Planning Strategies for Dynamic Opinion Networks
Papers with Code 아카이브
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/BigDataWUR/crop-gym
공식 코드CropGym: a Reinforcement Learning Environment for Crop Management
https://github.com/Bilibili/LastOrder
공식 코드Macro action selection with deep reinforcement learning in StarCraft
https://github.com/BitLhj/Ada-DyGNN
공식 코드Robust Knowledge Adaptation for Dynamic Graph Neural Networks
https://github.com/BlackHC/mdp
공식 코드https://github.com/BlueRiverTech/quanser-openai-driver
공식 코드Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware
https://github.com/BorealisAI/mtmfrl
공식 코드https://github.com/BorealisAI/pommerman-baseline
공식 코드Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
https://github.com/Breakend/ClimateChangeFromMachineLearningResearch
공식 코드Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
https://github.com/Breakend/EthicsInDialogue
공식 코드https://github.com/Breakend/OptionGAN
공식 코드https://github.com/Breakend/ReproducibilityInContinuousPolicyGradientMethods
공식 코드Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
https://github.com/Breakend/experiment-impact-tracker
공식 코드Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
https://github.com/BruntonUWBio/plumetracknets
공식 코드Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes
https://github.com/CAIR/deep-maze
공식 코드The Dreaming Variational Autoencoder for Reinforcement Learning Environments
https://github.com/CAiM-lab/PPO
공식 코드Reinforcement Learning of Musculoskeletal Control from Functional Simulations
https://github.com/CBCodeHub/Mining-Gym
공식 코드Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling
https://github.com/CDMCH/gym-fetch-stack
공식 코드https://github.com/CFMTech/Deep-RL-for-Portfolio-Optimization
공식 코드https://github.com/CIAM-Group/NCO_code/tree/main/single_objective/DPN-minmaxVRP-master
공식 코드DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
https://github.com/CIRLMDP/CIRL
공식 코드https://github.com/CLEANit/heatenginegym
공식 코드Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning
https://github.com/CLIVERCHEN/Marvel-O2O_Safe_RL
공식 코드Marvel: Accelerating Safe Online Reinforcement Learning with Finetuned Offline Policy
https://github.com/CN-TU/adversarial-recurrent-ids
공식 코드SparseIDS: Learning Packet Sampling with Reinforcement Learning
https://github.com/CN-TU/reinforcement-learning-for-per-flow-buffer-sizing
공식 코드LFQ: Online Learning of Per-flow Queuing Policies using Deep Reinforcement Learning
https://github.com/CODE-SUBMIT/simulator1
공식 코드Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes
https://github.com/CODE-SUBMIT/simulator2
공식 코드Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes
https://github.com/CORE-Robotics-Lab/ProLoNets
공식 코드Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning
https://github.com/CPJKU/score_following_game
공식 코드Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game
https://github.com/CPS-TUWien/racing_dreamer
공식 코드Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
https://github.com/CPS-research-group/dtsemnet
공식 코드https://github.com/CVC-Lab/SAC-for-H-Bond-Learning
공식 코드https://github.com/CausalML/DoubleReinforcementLearningMDP
공식 코드Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
https://github.com/CausalRL/DRL
공식 코드Deconfounding Reinforcement Learning in Observational Settings
https://github.com/Centre-for-Biorobotics/ML-soft-fin-motion-RoboSoft2025
공식 코드Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate Model
https://github.com/ChanganVR/RelationalGraphLearning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.