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/AliengirlLiv/xrl-dreamer
https://github.com/AmmarFayad/Behavioral-Actor-Critic
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/AndrewJWashington/protodriver
https://github.com/AnshMittal1811/AugmentedRandomSearch
Simple random search provides a competitive approach to reinforcement learning
https://github.com/AppliedDataSciencePartners/WorldModels
https://github.com/Aravind-11/AI-Gaming
https://github.com/Aravind-11/IITM_Saastra
https://github.com/Aravind-11/Multi-Agent-RL
https://github.com/Asap7772/understanding-rlhf
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
https://github.com/AurelianTactics/bcq_tensorflow
https://github.com/B-C-WANG/ReinforcementLearningInAutoPilot
https://github.com/BFAnas/BranchingDQN_keras
Action Branching Architectures for Deep Reinforcement Learning
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/TD3Agent.py
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/BY571/CQL
https://github.com/BY571/Normalized-Advantage-Function-NAF-
https://github.com/BY571/Soft-Actor-Critic-and-Extensions
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/Baichenjia/BHER
ARCHER: Aggressive Rewards to Counter bias in Hindsight Experience Replay
https://github.com/BerkeleyLearnVerify/VerifAI
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/Brook1711/RIS_components
https://github.com/CSautier/Breakout
https://github.com/CSautier/PongBot
https://github.com/CharlotteMorrison/Baxter-Research
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/CharlotteMorrison/Baxter-VREP
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/CharlotteMorrison/Baxter-VREP-Version-2
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/Christopheraburns/openai-gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/ChristosKap/policy_consolidation
https://github.com/Crevass/Hybrid-Agent
https://github.com/DHDev0/Muzero-unplugged
Online and Offline Reinforcement Learning by Planning with a Learned Model
https://github.com/DMIU-ShELL/MOSAIC
https://github.com/Dam930/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/DamienLancry/blocking_torcs
Simulated Car Racing Championship: Competition Software Manual
https://github.com/DamienLancry/torcs-1.3.4
Simulated Car Racing Championship: Competition Software Manual
https://github.com/DanielLSM/safe-rl-tutorial
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.