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/jyqhahah/rl_maddpg_matd3
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/kairproject/kair_algorithms_draft
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/kaixindelele/DRLib
Overcoming Exploration in Reinforcement Learning with Demonstrations
https://github.com/kalifou/robotics-rl-srl
Simple random search provides a competitive approach to reinforcement learning
https://github.com/karim-abdel/fact
https://github.com/kavosh8/MAC
https://github.com/kayuksel/pytorch-ars
Simple random search provides a competitive approach to reinforcement learning
https://github.com/kc-ml2/SimpleDreamer
https://github.com/kdally/fault-tolerant-flight-control-drl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/khansel01/nes-npg
https://github.com/kiminh/dual-policy-distillation
https://github.com/krasing/DRLearningCollaboration
https://github.com/krasing/DRLearningContinuousControl
https://github.com/ku2482/discor.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ku2482/gail-airl-ppo.pytorch
https://github.com/ku2482/rljax
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ku2482/rltorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/ku2482/slac.pytorch
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/ku2482/soft-actor-critic.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/kushagra06/DDPG
https://github.com/kushagra06/SAC
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/labmlai/annotated_deep_learning_paper_implementations
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/lanqingli1993/focal-iclr
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/lava-lab/safe-slac
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/lcswillems/torch-ac
https://github.com/learn-to-race/l2r
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/lgerrets/rl18-curiosity
https://github.com/liampetti/DDPG
https://github.com/ligy2016/gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/lim0606/pytorch-ardae-rl
https://github.com/lim0606/pytorch-ardae-vae
https://github.com/lionelblonde/sam-pytorch
Sample-Efficient Imitation Learning via Generative Adversarial Nets
https://github.com/liuyuezhang/pyrl
https://github.com/liyiyuian/Deep-Learning
https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.