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/seungju-k1m/sac-td3-td7
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/sgoldyaev/DeepFashion.ADGAN
Controllable Person Image Synthesis with Attribute-Decomposed GAN
https://github.com/shahabi8/Deep-Reinforcement-Learning
https://github.com/shahin-01/vqa-ad
https://github.com/shakedzy/warehouse
https://github.com/shamanez/Variational-Discriminator-Bottleneck-Tensorflow-Implementation
https://github.com/shehrum/RL_Continous-Control
https://github.com/shinian123/gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/shiningsunnyday/mcts-chess
https://github.com/shreyasc-13/off_domain_rlkit
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
https://github.com/shreyesss/PPO-implementation-keras-tensorflow
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/shriram2112/Reinforcement_learning_gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/shuishida/soaprl
https://github.com/shyamsn97/hyper-nn
https://github.com/siddharthverma314/proximalpolicyoptimization
https://github.com/silvialuu/DRL-2018
https://github.com/simonzhan-code/step-wise_saferl_pixel
https://github.com/sirakik/mprg_fc
https://github.com/sirakik/ppo_football
https://github.com/sisl/event-driven-rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sisl/gail-driver
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/siyuofzhou/DDPG
https://github.com/sliao-mi-luku/DeepRL-multiple-agents-tennis-udacity-drlnd-p3
https://github.com/smj007/Breakout_A3C
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/songrotek/DDPG
https://github.com/soumik12345/DDPG
https://github.com/soumik12345/Twin-Delayed-DDPG
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/sparkmxy/my-offlinerl
https://github.com/stevenpjg/RDPG
https://github.com/stevenpjg/ddpg-aigym
https://github.com/subinlab/model_based_rl_paper
Self-supervised Learning of Image Embedding for Continuous Control
https://github.com/sumkumar/hiro_impl
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
https://github.com/sunfex/weighted-sac
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/susan-amin/SparseBaseline1
https://github.com/susanwe/ope_worldmodels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.