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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/navid-naderi/GraphMIX
공식 코드Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning
https://github.com/navneet-nmk/pytorch-rl
공식 코드Empowerment-driven Exploration using Mutual Information Estimation
https://github.com/nazmul-karim170/RL-NCS
공식 코드RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing
https://github.com/needleworm/greenhouse_ai
공식 코드Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learning
https://github.com/nesl/eagle_ptz_cameras
공식 코드Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras
https://github.com/netease-youdao/confucius3-math
공식 코드Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning
https://github.com/neunms/reinforcement-learning-on-graph-a-survey
공식 코드https://github.com/neunms/reinforcement-learning-on-graphs-a-survey
공식 코드https://github.com/neurasearch/spotify-xrl-skipping-prediction
공식 코드Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning
https://github.com/neuromorphiccomputing/stpn
공식 코드https://github.com/ngminhtri0394/MACDA
공식 코드Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction
https://github.com/ngmq/adversarial-online-multi-task-reinforcement-learning
공식 코드https://github.com/nguyendohoangkhoiUF/Swift-Hydra
공식 코드Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models
https://github.com/nhatch/rrt
공식 코드The Value of Planning for Infinite-Horizon Model Predictive Control
https://github.com/nic5472k/scientificdata2021_healthgym
공식 코드https://github.com/nickkantack/Hanabi-Cyclone
공식 코드Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates
https://github.com/nicklashansen/dmcontrol-generalization-benchmark
공식 코드Generalization in Reinforcement Learning by Soft Data Augmentation
https://github.com/nicob15/state_representation_learning_methods
공식 코드Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
https://github.com/nicoladainese96/code-world-models
공식 코드Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
https://github.com/nicolinho/acc
공식 코드Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
https://github.com/nicolinho/dutd
공식 코드Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
https://github.com/nicomeyer96/qrl-benchmark
공식 코드https://github.com/nikeke19/safe-mult-rl
공식 코드A Multiplicative Value Function for Safe and Efficient Reinforcement Learning
https://github.com/nikonikolov/rltf
공식 코드Information-Directed Exploration for Deep Reinforcement Learning
https://github.com/nikunj-gupta/on-the-calibration-of-compartmental-epidemiological-models
공식 코드https://github.com/ninglab/sapient
공식 코드https://github.com/niravajmeri/siga-ijcai2022
공식 코드Socially Intelligent Genetic Agents for the Emergence of Explicit Norms
https://github.com/niupuhua1234/gfn-pg
공식 코드https://github.com/nju-rl/meta-dt
공식 코드Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
https://github.com/njustesen/a2c_gvgai
공식 코드Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
https://github.com/nnaisense/evotorch
공식 코드https://github.com/nnaisense/max
공식 코드https://github.com/nnbay/MeicalChatbot-HRL
공식 코드Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
https://github.com/nobodymx/sorl-for-auto-bidding
공식 코드https://github.com/nobukatsu-kajiura/Self-Play-RL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.