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/facebookresearch/ReAgent
https://github.com/facebookresearch/denoised_mdp
https://github.com/facebookresearch/gwil
https://github.com/facebookresearch/rl
https://github.com/facebookresearch/rl/blob/main/examples/ddpg/ddpg.py
https://github.com/facebookresearch/rl/blob/main/examples/sac/sac.py
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/facebookresearch/slbo
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
https://github.com/fdcl-gwu/gym-rotor
https://github.com/feruxhi/thoughts
https://github.com/feryal/craft-env
Modular Multitask Reinforcement Learning with Policy Sketches
https://github.com/fgitmichael/SVAEfromSLAC
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/fgitmichael/TestModeDisentangling
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/fhbzc/FishAgentSimulation
https://github.com/fiberleif/nc_ddpg
https://github.com/flavioschneider/ml_papers_presentations
https://github.com/floodsung/DDPG
https://github.com/flowersteam/curious
https://github.com/flowersteam/geppg
https://github.com/flowersteam/rl_stats
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/fomorians-oss/awr
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
https://github.com/fomorians/forward-models
https://github.com/fshamshirdar/pytorch-rdpg
https://github.com/fusion-ml/trajectory-information-rl
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/gaetanserre/l2rpn-2022_ppo-baseline
https://github.com/gaetanserre/lipo
https://github.com/garyzyr001/rethinking-airl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/georgesung/TD3
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/georgkruse/cleanqrl
https://github.com/ghliu/pytorch-ddpg
https://github.com/ghostFaceKillah/expert
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/gijskoning/ReproducingCURL
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/gmoss1/Kaggle-Halite-IV-RL
https://github.com/goncharom/PPOv1
https://github.com/google-deepmind/csil
https://github.com/google-deepmind/spriteworld
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.