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/apourchot/EvoLearn
https://github.com/araffin/sbx
https://github.com/aravindsrinivas/curl_rainbow
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
https://github.com/arnomoonens/yarll
https://github.com/arrival-ltd/catalyst-rl-tutorial
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/arunavkonwar/world-models
https://github.com/ashaaher/Reinforcement-Learning-Project
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/asparius/barlowrl
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
https://github.com/aspk/Tensorflow_Deep_RL
https://github.com/automl/learna
https://github.com/autumnwu/aggressive-q-learning-with-ensembles
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/aviralkumar2907/BEAR
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
https://github.com/avivelor/UnityMachineLearningForProjectButterfly
https://github.com/axnedergaard/normalized-advantage-functions
https://github.com/b06b01073/Twin-Delayed-DDPG
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/b06b01073/continuous-control
https://github.com/bacdavid/HER
https://github.com/backgom2357/Recommender_system_via_deep_RL
https://github.com/bastienvanderplaetse/action-branching-dqn
Action Branching Architectures for Deep Reinforcement Learning
https://github.com/baturaysaglam/RIS-MISO-Deep-Reinforcement-Learning
https://github.com/baturaysaglam/TCD3
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods
https://github.com/baturaysaglam/ris-miso-pda-deep-reinforcement-learning
Deep Intrinsically Motivated Exploration in Continuous Control
https://github.com/bay3s/ppo-parallel
https://github.com/bay3s/ppo-rl
https://github.com/benevolentAI/guacamol_baselines
https://github.com/benpetit/cs379c
https://github.com/bentrevett/pytorch-rl
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/bidimensional/Tennis
https://github.com/biemann/Continuous-Control
https://github.com/billchan226/poar-srl-4-robot
Simple random search provides a competitive approach to reinforcement learning
https://github.com/bitterbloom/Reinforcement-Learning
https://github.com/bmeyers/VirtualMicrogridSegmentation
https://github.com/bonniesjli/PPO-Reacher_UnityML
https://github.com/bonniesjli/PPO_Reacher
https://github.com/brain-research/mirage-rl
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.