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/ray-project/ray/tree/master/rllib
https://github.com/rcheng805/RL-CBF
https://github.com/rddy/ReQueST
Learning Human Objectives by Evaluating Hypothetical Behavior
https://github.com/rehamessameltagoury/Urban-Self-Driving-Car
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
https://github.com/rehoss/lbmpc_semimarkov
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/reinforcement-learning-kr/lets-do-irl
https://github.com/reinforcement-learning-kr/pg_travel
https://github.com/reiniscimurs/TD3_Separate_Action
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/rejuvyesh/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/richardrl/cartpole-request-for-research
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rikluost/RL_DQN_Pong
https://github.com/rk1998/robot-sac
https://github.com/rll/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rlworkgroup/garage
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/robintyh1/icml2021-pengqlambda
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/roythuly/obac
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/rperezdattari/Interactive-Learning-with-Corrective-Feedback-for-Policies-based-on-Deep-Neural-Networks
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
https://github.com/rshnn/battleship
https://github.com/runamu/compositional-conservatism
https://github.com/russellmendonca/maesn_suite
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/s-sd/task-amenability
https://github.com/safe-slac/safe-slac
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/sail-sg/offbench
https://github.com/samiranrl/ODSC_2019_RL
https://github.com/samuel500/World-Models
https://github.com/samuelmat19/DDPG-tf2
https://github.com/santolucito/Haskell-TORCS
Simulated Car Racing Championship: Competition Software Manual
https://github.com/saoudh/Reinforcement-Learning
https://github.com/sarmueller/gibo
Simple random search provides a competitive approach to reinforcement learning
https://github.com/saschaschramm/Pong
https://github.com/sayantanauddy/hierarchical_bipedal_controller
https://github.com/sbodenstein/Tutorials
Simple random search provides a competitive approach to reinforcement learning
https://github.com/sc2crazy/StarCrackRL
https://github.com/schatty/D4PG-pytorch
https://github.com/scottemmons/youngs-cql
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.