https://github.com/adaptive-intelligent-robotics/pga-map-elites
Addressing Function Approximation Error in Actor-Critic Methods
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.
Addressing Function Approximation Error in Actor-Critic Methods
High-Dimensional Continuous Control Using Generalized Advantage Estimation
TD-MPC2: Scalable, Robust World Models for Continuous Control
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
High-Dimensional Continuous Control Using Generalized Advantage Estimation
Simple random search provides a competitive approach to reinforcement learning
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Safe Continuous Control with Constrained Model-Based Policy Optimization
Simulated Car Racing Championship: Competition Software Manual
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.