https://github.com/xiuyu0000/new_papers_codes/tree/main/sac
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Addressing Function Approximation Error in Actor-Critic Methods
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Addressing Function Approximation Error in Actor-Critic Methods
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
REBEL: Reinforcement Learning via Regressing Relative Rewards
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.