https://github.com/han-adam/generalanalyze
공식 코드Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System
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/hanglai/autombpo
공식 코드On Effective Scheduling of Model-based Reinforcement Learning
https://github.com/hansenhua/ilid-offline-imitation-learning
공식 코드How to Leverage Diverse Demonstrations in Offline Imitation Learning
https://github.com/hansenhua/ollie-offline-to-online-imitation-learning
공식 코드OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
https://github.com/harrylui98/zoac-tevc
공식 코드Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems
https://github.com/hehongc/DCMRL
공식 코드Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills
https://github.com/herolab-uga/bsac
공식 코드Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning
https://github.com/hmishfaq/lsac
공식 코드Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning
https://github.com/hnyu/taac
공식 코드TAAC: Temporally Abstract Actor-Critic for Continuous Control
https://github.com/holarissun/rewardshifting
공식 코드Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping
https://github.com/huanzhang12/ATLA_robust_RL
공식 코드Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning
공식 코드Action Robust Reinforcement Learning and Applications in Continuous Control
https://github.com/idsia/gogepo
공식 코드https://github.com/idsia/policyevaluator
공식 코드General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
https://github.com/ihuhuhu/RAC
공식 코드Balancing Value Underestimation and Overestimation with Realistic Actor-Critic
https://github.com/iitkcpslab/rlstl
공식 코드STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning
https://github.com/indylab/delayeddreamer
공식 코드Reinforcement Learning from Delayed Observations via World Models
https://github.com/intel-isl/LMRS
공식 코드https://github.com/intelai/cerl
공식 코드https://github.com/intelligent-control-lab/absolute-policy-optimization
공식 코드https://github.com/intelligent-reliable-autonomous-systems/gcrs-expert-abstractions
공식 코드Learning with Expert Abstractions for Efficient Multi-Task Continuous Control
https://github.com/isluoshuang/macfn
공식 코드Multi-Agent Continuous Control with Generative Flow Networks
https://github.com/jaayala/risk_aware_contextual_bandit
공식 코드Risk-Aware Continuous Control with Neural Contextual Bandits
https://github.com/jacobandreas/psketch
공식 코드Modular Multitask Reinforcement Learning with Policy Sketches
https://github.com/jacooba/hyper
공식 코드https://github.com/jakegrigsby/deep_control
공식 코드Towards Automatic Actor-Critic Solutions to Continuous Control
https://github.com/jakegrigsby/dmc_remastered
공식 코드Measuring Visual Generalization in Continuous Control from Pixels
https://github.com/janscholten/ppmp
공식 코드https://github.com/jaysparrow/rl-for-active-mapping
공식 코드Learning Continuous Control Policies for Information-Theoretic Active Perception
https://github.com/jdchang1/milo
공식 코드Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
https://github.com/jhoon-cho/mbtl
공식 코드Model-Based Transfer Learning for Contextual Reinforcement Learning
https://github.com/jigangkim/autoreset_rl
공식 코드https://github.com/jihwan-jeong/cbop
공식 코드Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
https://github.com/jincate/ccep
공식 코드Centralized Cooperative Exploration Policy for Continuous Control Tasks
https://github.com/jinzhuluo/euclideanda
공식 코드Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control
https://github.com/joelouismarino/variational_rl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.