Papers with Code 아카이브
Continuous Control
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/matt00n/policygradientsjax
공식 코드https://github.com/matthias-weissenbacher/Quadratic_MLPs_in_RL
공식 코드A Quadratic Actor Network for Model-Free Reinforcement Learning
https://github.com/max7born/decision-lstm
공식 코드https://github.com/mees/calvin
공식 코드https://github.com/metekemertas/RobustBisimulation
공식 코드https://github.com/metekemertas/api-bisim
공식 코드https://github.com/michael-beukman/robocupgym
공식 코드RobocupGym: A challenging continuous control benchmark in Robocup
https://github.com/microsoft/GFB-audio-control
공식 코드Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data
https://github.com/microsoft/atac
공식 코드Adversarially Trained Actor Critic for Offline Reinforcement Learning
https://github.com/mikezhang95/rrl_usr
공식 코드Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization
https://github.com/mingzhangPHD/Adversarial-Imitation-Learning
공식 코드Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration
https://github.com/mlech26l/neural_martingales
공식 코드Learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic Systems
https://github.com/modestyachts/ARS
공식 코드Simple random search provides a competitive approach to reinforcement learning
https://github.com/monnalo/uav_navigation
공식 코드UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment
https://github.com/montrealrobotics/iv_rl
공식 코드Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
https://github.com/muttimirco/mepol
공식 코드Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate
https://github.com/nathanrahn/return-landscapes
공식 코드Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
https://github.com/naumix/BiggerRegularizedOptimistic
공식 코드Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control
https://github.com/neurips-2019/GAC
공식 코드Distributional Policy Optimization: An Alternative Approach for Continuous Control
https://github.com/neuro-ai-robotics/OBR
공식 코드Object-centric proto-symbolic behavioural reasoning from pixels
https://github.com/neurorave/neurorave
공식 코드Continuous descriptor-based control for deep audio synthesis
https://github.com/nicklashansen/tdmpc
공식 코드https://github.com/nicolinho/acc
공식 코드Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning
https://github.com/nnaisense/MAGE
공식 코드How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization
https://github.com/nnaisense/pgpelib
공식 코드ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution
https://github.com/noc-lab/sac-expert
공식 코드https://github.com/noetits/ICE-Talk
공식 코드Analysis and Assessment of Controllability of an Expressive Deep Learning-based TTS system
https://github.com/npu-rcir/online_ccm
공식 코드Enhanced Robust Tracking Control: An Online Learning Approach
https://github.com/ntavanhoeffelen/daif_carracing
공식 코드Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem
https://github.com/oh-lab/APPO
공식 코드Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning
https://github.com/oist-cnru/ReMASTER
공식 코드https://github.com/openai/baselines
공식 코드Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
https://github.com/oxwhirl/loaded-dice
공식 코드https://github.com/papercode2022/hill
공식 코드https://github.com/pemami4911/deep-rl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.