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/toshikwa/discor.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/toshikwa/slac.pytorch
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
https://github.com/toshikwa/soft-actor-critic.pytorch
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/tphanson/xupr-drl
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
https://github.com/trackmania-rl/tmrl
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/tsinghua-rll/gym-gridworld
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/tuanpnm99/RLPongAgent
https://github.com/twni2016/f-IRL
A Divergence Minimization Perspective on Imitation Learning Methods
https://github.com/uber-research/ga-world-models
https://github.com/ugo-nama-kun/gym_torcs
Simulated Car Racing Championship: Competition Software Manual
https://github.com/unstablecursor/robotino_rl
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
https://github.com/uoe-agents/derl
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
https://github.com/uvipen/super-mario-bros-ppo-pytorch
https://github.com/vaibhavsaxena11/cwvae
https://github.com/varuncs2011/rl
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2
https://github.com/vermashresth/damage-aware-PPO
https://github.com/vgudapati/DRLND_Collaboration_Competetion
https://github.com/vheuthe/microbot_rl
https://github.com/voot-t/ril_co
https://github.com/vu-resilient-distributed-systems/assignment-1-fall-19
Simulated Car Racing Championship: Competition Software Manual
https://github.com/vub-ai-lab/qwr
Q-Value Weighted Regression: Reinforcement Learning with Limited Data
https://github.com/vvanirudh/imitation-learning-gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/wangshub/RL-Stock
https://github.com/wangzhengfei0730/NIPS2018-AIforProsthetics
https://github.com/wassname/rl-portfolio-management
https://github.com/watchernyu/spinningup-drl-prototyping
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/willwhitney/dynamics-aware-embeddings
https://github.com/wpiszlogin/driver_critic
https://github.com/wwydmanski/rl_tennis
https://github.com/wyndwarrior/imitation_from_observation
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/xbpeng/awr
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
https://github.com/xiawenwen49/ppo
https://github.com/xingyu-lin/softagent
https://github.com/xiuyu0000/new_papers_codes/tree/main/ppo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.