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/lollcat/Soft-Actor-Critic
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/lqnew/continuous_control_benchmark
https://github.com/lucianzhong/DDPG_to_drive_in_TORCS
Simulated Car Racing Championship: Competition Software Manual
https://github.com/luke-bhan/TwitterSentimentAnalysisTool
https://github.com/lukebhan/TwitterSentimentAnalysisTool
https://github.com/lyp741/Pytorch-ERL
https://github.com/m5823779/DDPG
https://github.com/m5823779/MotionPlannerUsingDDPG
https://github.com/madhur-tandon/RL-Project
https://github.com/madvn/DDPG
https://github.com/magnusja/ppo
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/majercakdavid/gym-virtual-quant-trading
https://github.com/mark-gluzman/NmodelPPO
https://github.com/markub3327/rl-agent
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/markub3327/rl-baselines
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/markub3327/rl-toolkit
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/marload/DeepRL-TensorFlow2
https://github.com/massquantity/DBRL
https://github.com/matejker/controllability-of-complex-networks
A Tour of Reinforcement Learning: The View from Continuous Control
https://github.com/mathformoso/RL-playground---ddpg
https://github.com/matthewsparr/Reinforcement-Learning-Lesson
https://github.com/maximerobeyns/taylortd
How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
https://github.com/maziarg/PrivAttack-BCQ
https://github.com/mazpie/mastering-urlb
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
https://github.com/mbhenaff/EEN
Query-Efficient Imitation Learning for End-to-End Autonomous Driving
https://github.com/mbrucker07/experiments
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/mees/Adversarial-Skill-Networks
Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video
https://github.com/mehdimashayekhi/Some-RL-Implementation
https://github.com/menyifang/adgan
Controllable Person Image Synthesis with Attribute-Decomposed GAN
https://github.com/michael-snower/ppo
https://github.com/microsoft/Mask-based-Latent-Reconstruction
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
https://github.com/microsoft/lightatac
Adversarially Trained Actor Critic for Offline Reinforcement Learning
https://github.com/microsoft/strategically_efficient_rl
https://github.com/mightypirate1/DRL-Tetris
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/mightypirate1/PPO_homebrew
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.