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/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore
https://github.com/minhphd/PyDreamerV1
https://github.com/miroblog/tf_deep_rl_trader
https://github.com/mit-realm/neuriss
https://github.com/mklissa/dceo
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing
https://github.com/monoclecat/latent-conditioned-SAC
https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics
https://github.com/monte-ai/expert-augmented-acktr
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/moreanp/csro
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/morikatron/GAIL_PPO
https://github.com/morikatron/PPO
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/mxblr/DeepRLHockey
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/myo-manipulation/spasticmyoelbow
MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control
https://github.com/myohub/myosuite
MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control
https://github.com/nagisazj/idaq_public
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/naruya/vgan-pytorch
https://github.com/nav74neet/ddpg4biped
https://github.com/nav74neet/ddpg_biped
https://github.com/nav74neet/rl4biped
https://github.com/near32/regym
https://github.com/neilsgp/RL-Algorithms
https://github.com/neuroevolution-ai/ProcgenAutoencoder
https://github.com/nhynes/abc
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
https://github.com/nicklashansen/tdmpc2
https://github.com/nikhilbarhate99/PPO
https://github.com/nikhilbarhate99/PPO-PyTorch
https://github.com/nikhilbarhate99/TD3-PyTorch-BipedalWalker-v2
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/nitthilan/pommerman
https://github.com/nmsquared/CS7641-Assignment-4
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/nohboogy/gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/nric/ProximalPolicyOptimizationContinuousKeras
https://github.com/nvlabs/gbrl_sb3
https://github.com/obster991/RL-model-based-planning-from-scratch
https://github.com/octothrope/ARS
Simple random search provides a competitive approach to reinforcement learning
https://github.com/oh-lab/UTE-Uncertainty-aware-Temporal-Extension-
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.