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/xbpeng/mcp
공식 코드MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
https://github.com/xuanlinli17/iclr2021_rlreg
공식 코드https://github.com/xuanlinli17/po-rl-regularization
공식 코드https://github.com/xujinming01/td3aq
공식 코드https://github.com/xupei0610/PFPN
공식 코드PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network
https://github.com/yannickycpan/reproducerl
공식 코드Understanding and Mitigating the Limitations of Prioritized Experience Replay
https://github.com/yeshenpy/erl-re2
공식 코드https://github.com/yingchengyang/BIRIS
공식 코드https://github.com/yingchengyang/CPPO
공식 코드Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
https://github.com/yingfan-bot/mbpsrl
공식 코드Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
https://github.com/yjhuangcd/fi-ode
공식 코드FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs
https://github.com/yobibyte/amorpheus
공식 코드My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
https://github.com/zdhnarsil/stochastic-marginal-actor-critic
공식 코드Latent State Marginalization as a Low-cost Approach for Improving Exploration
https://github.com/zhangxz1123/FilteredPolicyGradient
공식 코드https://github.com/zhaolingao/rebel
공식 코드REBEL: Reinforcement Learning via Regressing Relative Rewards
https://github.com/zhihanyang2022/off-policy-continuous-control
공식 코드Recurrent Off-policy Baselines for Memory-based Continuous Control
https://github.com/zhixuan-lin/ensemble-rl-continuous
공식 코드https://github.com/zhixuan-lin/ensemble-rl-discrete
공식 코드https://github.com/zhu-edward/dgsqp
공식 코드A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria
https://github.com/zhuyuanyang/udprl
공식 코드https://github.com/ziqian2000/smiling
공식 코드Diffusing States and Matching Scores: A New Framework for Imitation Learning
https://github.com/zjlab-ammi/hs-omrl
공식 코드On Context Distribution Shift in Task Representation Learning for Offline Meta RL
https://github.com/zwfightzw/Meta-Critic
공식 코드Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
https://bitbucket.org/act-lab/release
https://github.com/09jvilla/CS234_gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/1110589721/openia-gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning
https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDAG
https://github.com/2mawi2/master-thesis-experiments
https://github.com/62subinh/deeprl_safety_specification
https://github.com/ASzot/ppo-pytorch
https://github.com/AdeelMufti/WorldModels
https://github.com/AgrawalAmey/safe-explorer
https://github.com/AlexZhaoZt/Temporal_Leap_HRL
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.