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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/Jeffrey-Ede/Adaptive-Partial-STEM

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Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning

https://github.com/Jeffrey-Ede/adaptive-scans

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Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning

https://github.com/JetsGame/GroomRL

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Jet grooming through reinforcement learning

https://github.com/JetsGame/libGroomRL

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Jet grooming through reinforcement learning

https://github.com/JianGuanTHU/IRecGAN

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Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation

https://github.com/JieRen98/rlkit-pmoe

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Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

https://github.com/JinyuZ1996/RL-ISN

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Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

https://github.com/Joe-zsc/GAP

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Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning

https://github.com/JohanSamir/revisiting_rainbow

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Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

https://github.com/JohannesAck/MATD3implementation

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Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

https://github.com/JohnnyYeeee/math_prog_synth_env

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A Reinforcement Learning Environment for Mathematical Reasoning via Program Synthesis

https://github.com/KDL-umass/saliency_maps

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Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning

https://github.com/Kaijin1996/SCAOPO

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Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning

https://github.com/KaijuML/PARENTing-rl

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PARENTing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text Generation

https://github.com/KaiyangZhou/vsumm-reinforce

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Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

https://github.com/Kapilan-Balagopalan/Linear-Bandit-Algorithms

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Minimum Empirical Divergence for Sub-Gaussian Linear Bandits

https://github.com/Karim-53/Reinforcement-Learning-Malaria-Control-KDDCup-2019

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Policy Learning for Malaria Control

https://github.com/KarlXing/LUSR

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Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation

https://github.com/KevinEloff/rl-collaborative-communication

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Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language

https://github.com/Khrylx/EgoPose

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Ego-Pose Estimation and Forecasting as Real-Time PD Control

https://github.com/KuangenZhang/ATD3

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Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics

https://github.com/KunalP117/YouTube_project

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Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior

https://github.com/KyunghyunLee/aes-rl

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An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search

https://github.com/LAIRLAB/ARS-experiments

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Exploration in Action Space

https://github.com/LAIRLAB/contrasting_exploration_rl

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Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective

https://github.com/LAMDA-RL/CoLA

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Controlling Large Language Model with Latent Actions

https://github.com/LARG/regression-importance-sampling

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Importance Sampling Policy Evaluation with an Estimated Behavior Policy

https://github.com/LCAS/deep_rl_for_swarms

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Deep Reinforcement Learning for Swarm Systems

https://github.com/LTL2Action/LTL2Action

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LTL2Action: Generalizing LTL Instructions for Multi-Task RL

https://github.com/Lamperougeyxy/GHQ

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GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning

https://github.com/LantaoYu/SeqGAN

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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/LeCAR-Lab/SafeDPA

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Safe Deep Policy Adaptation

https://github.com/LeaMarion/quantum-enhanceable-deep-rl

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Quantum enhancements for deep reinforcement learning in large spaces

https://github.com/Lei-Kun/Uni-O4

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Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization

https://github.com/LiangZhang1996/Advanced_XLight

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Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

https://github.com/LiangZhang1996/DataLight

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DataLight: Offline Data-Driven Traffic Signal Control

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