Continuous Control
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Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JL321/mujo-2DWalker
https://github.com/Ji4chenLi/ARS_experiment
Simple random search provides a competitive approach to reinforcement learning
https://github.com/Jingliang-Duan/Distributional-Soft-Actor-Critic
https://github.com/JohannesAck/MATD3implementation
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/JonasRSV/DDPG
https://github.com/JonasRSV/DQNTensorflow
https://github.com/JonasRSV/PGTensorflow
https://github.com/JonasRSV/PPO
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/JorgeA-RD/Reinforcement-Learning-Agents/tree/main/DDPG
https://github.com/KAIST-AILab/deeprl_practice_colab
https://github.com/Kaixhin/ACER
https://github.com/Kaixhin/PlaNet
https://github.com/Kaixhin/imitation-learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/Kaixhin/spinning-up-basic
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
https://github.com/KarlXing/RL-Visual-Continuous-Control
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
https://github.com/KelvinYang0320/deepbots-panda
https://github.com/Khrylx/PyTorch-RL
https://github.com/Kikumu/Reinforcement-Learning-repo
https://github.com/KohMat/carracing-dreamer
https://github.com/LM095/DDPG-implementation
https://github.com/LeoToledo/PivotingTaskRL
https://github.com/LuEE-C/PPO-Keras
https://github.com/LucasSilve/openAIgym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/MLCS-Yonsei/ddpg-control
https://github.com/MagaliDrumare/Augmented-Random-Search-ARS-Intuition
Simple random search provides a competitive approach to reinforcement learning
https://github.com/MaksimIM/aml
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
https://github.com/MarsEleven/car_racer_RL
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/MathPhysSim/PER-NAF
https://github.com/MatteoBrentegani/PPO
https://github.com/MatthieuSarkis/Portfolio-Optimization-and-Goal-Based-Investment-with-Reinforcement-Learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Proximal_Policy_Optimization_Algorithms
https://github.com/Medabid1/RL_Project
https://github.com/Michaelrising/Prog-RL
https://github.com/MilkSilk/masters_thesis
https://github.com/MindSpore-scientific-2/code-8/tree/main/SV-X-Softmax
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.