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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/AaltoVision/realant-rl

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RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning

https://github.com/Abdulmajid-Murad/IoT-Sensor-Gym

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Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning

https://github.com/Abdulmajid-Murad/adaptive-sensing

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Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning

https://github.com/AboudyKreidieh/h-baselines

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Inter-Level Cooperation in Hierarchical Reinforcement Learning

https://github.com/ActionEmbedding/ActionEmbedding

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Learning Action-Transferable Policy with Action Embedding

https://github.com/AdamStelmaszczyk/learning2run

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Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

https://github.com/AlexBeesonWarwick/OfflineRLFactorisableActionSpaces

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An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces

https://github.com/AlexandreChenu/DCIL_XPAG

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Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration

https://github.com/Algolzw/SPAC-Deformable-Registration

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Stochastic Actor-Executor-Critic for Image-to-Image Translation

https://github.com/AliBeikmohammadi/TA-Explore

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Human-Inspired Framework to Accelerate Reinforcement Learning

https://github.com/Altriaex/multi_source_behavior_modeling

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Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning

https://github.com/AmazingAng/VER

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Revisiting Prioritized Experience Replay: A Value Perspective

https://github.com/AmiiThinks/mon_mdp_neurips24

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Beyond Optimism: Exploration With Partially Observable Rewards

https://github.com/Amyyyyeah/ARES

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ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback

https://github.com/AndreaCossu/ContinualLearning-SequentialProcessing

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Continual Learning with Gated Incremental Memories for sequential data processing

https://github.com/Antymon/ppo_cpp

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Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulations

https://github.com/AnujMahajanOxf/VIREL

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VIREL: A Variational Inference Framework for Reinforcement Learning

https://github.com/ApricityZ/TERL

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TERL: Large-Scale Multi-Target Encirclement Using Transformer-Enhanced Reinforcement Learning

https://github.com/ArantxaCasanova/ralis

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Reinforced active learning for image segmentation

https://github.com/Ardavans/DSR

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Deep Successor Reinforcement Learning

https://github.com/ArronDZhang/ROLeR

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ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems

https://github.com/Asap7772/understanding-rlhf

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Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data

https://github.com/BIT-MCS/Cadre

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CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving

https://github.com/BME-SmartLab/rl-wds

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Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems

https://github.com/BNN-UPC/ENERO

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ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning

https://github.com/BU-DEPEND-Lab/DRIBO

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DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck

https://github.com/BaiLiping/Coaching

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Accelerate Reinforcement Learning with PID Controllers in the Pendulum Simulations

https://github.com/BaiLiping/WirelessPowerTransfer

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Multiagent Reinforcement Learning based Energy Beamforming Control

https://github.com/Baichenjia/OB2I

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Principled Exploration via Optimistic Bootstrapping and Backward Induction

https://github.com/Bam4d/conditional-action-trees

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Generalising Discrete Action Spaces with Conditional Action Trees

https://github.com/BarakGahtan/5GRIE

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5G Routing Interfered Environment

https://github.com/BartvLaatum/GreenLight-Gym2

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GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems

https://github.com/Bellman-devs/bellman

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Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow

https://github.com/BeloborodovDS/SIMCIM-RL

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Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization

https://github.com/Benjamin-eecs/Theoretical-GMRL

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A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

https://github.com/BerenMillidge/Active_Inference_Successor_Representations

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Successor Representation Active Inference

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