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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/TheLeprechaun25/neural-improvement-heuristics

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Neural Improvement Heuristics for Graph Combinatorial Optimization Problems

https://github.com/ThibautTheate/An-Application-of-Deep-Reinforcement-Learning-to-Algorithmic-Trading

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An Application of Deep Reinforcement Learning to Algorithmic Trading

https://github.com/ThibautTheate/Unconstrained-Monotonic-Deep-Q-Network-algorithm

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Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks

https://github.com/Thinklab-SJTU/awesome-ml4co

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Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching

https://github.com/TimSalimans/weight_norm

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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/Tinker-Twins/MARL-Simulator

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Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement Learning

https://github.com/TonghanWang/EITI-EDTI

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Influence-Based Multi-Agent Exploration

https://github.com/ToniCreswell/Adversarial-Attack-On-Latent-Space

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LatentPoison - Adversarial Attacks On The Latent Space

https://github.com/TorchCraft/TorchCraftAI

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Growing Action Spaces

https://github.com/Total-RD/pymgrid

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pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research

https://github.com/Tsinghua-Space-Robot-Learning-Group/SpaceRobotEnv

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A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative Object

https://github.com/Tviskaron/pogema-baselines/tree/main/pymarl

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Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments

https://github.com/UCLA-StarAI/Analogous-Disentangled-Actor-Critic

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Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration

https://github.com/UCR-Intelligent-Robotics-Lab/greedy_agent

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PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas

https://github.com/UCSB-NLP-Chang/ThinkPrune

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ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

https://github.com/UKPLab/emnlp2018-april

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APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

https://github.com/UKPLab/ijcai2019-relis

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Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

https://github.com/UKPLab/irj-neural-april

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Preference-based Interactive Multi-Document Summarisation

https://github.com/UKPLab/refresh2018-predicting-trends-from-arxiv

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Predicting Research Trends From Arxiv

https://github.com/UMich-ML-Group/RL-Ridesharing

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Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning

https://github.com/USTC-IMCL/Task-driven-Semantic-Coding-via-RL

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Task-driven Semantic Coding via Reinforcement Learning

https://github.com/UT-Austin-RPL/maple

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Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks

https://github.com/Unakar/Logic-RL

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Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning

https://github.com/Unity-Technologies/ml-agents

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On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning

https://github.com/UoA-CARES/cares_reinforcement_learning/blob/main/cares_reinforcement_learning/algorithm/policy/CTD4.py

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CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics

https://github.com/UofT-EcoSystem/rlscope

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RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads

https://github.com/Usama1002/EBE-Exploration

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Learning-Driven Exploration for Reinforcement Learning

https://github.com/ValentinaZangirolami/DRL

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Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning

https://github.com/VidhushiniSrinivasan16/ICMLA

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Natural Language Generation Using Reinforcement Learning with External Rewards

https://github.com/ViktoriaSchmiesing/RL_Fiber_Coupling

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Model-free reinforcement learning with noisy actions for automated experimental control in optics

https://github.com/VinF/deer

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Combined Reinforcement Learning via Abstract Representations

https://github.com/VittorioGiammarino/Learning-from-humans-combining-imitation-and-deep-on-policy-reinforcement-learning-to-accomplish-su

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Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task

https://github.com/VittorioGiammarino/Online_BWforHIL

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Online Baum-Welch algorithm for Hierarchical Imitation Learning

https://github.com/VowpalWabbit/slope-experiments

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Adaptive Estimator Selection for Off-Policy Evaluation

https://github.com/Vvrep/MASTER-electric_vehicle_charging_recommendation

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Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning

https://github.com/WMGDataScience/gym_wmgds

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Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.