https://github.com/baggepinnen/LPVSpectral.jl
공식 코드Machine Learning and System Identification for Estimation in Physical Systems
Papers with Code 아카이브
Model-based Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/baggepinnen/LTVModels.jl
공식 코드Machine Learning and System Identification for Estimation in Physical Systems
https://github.com/baggepinnen/Robotlib.jl
공식 코드Machine Learning and System Identification for Estimation in Physical Systems
https://github.com/baimingc/dambrl
공식 코드Delay-Aware Model-Based Reinforcement Learning for Continuous Control
https://github.com/batra-mlp-lab/va_mbrl
공식 코드https://github.com/bcornelusse/microgridRLsimulator
공식 코드Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
https://github.com/befelix/safe_learning
공식 코드Safe Model-based Reinforcement Learning with Stability Guarantees
https://github.com/ben-eysenbach/mnm
공식 코드Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
https://github.com/biorobotics/learning_modular_policies
공식 코드https://github.com/bit1029public/hrssm
공식 코드Learning Latent Dynamic Robust Representations for World Models
https://github.com/blakeyy/neuromorphic_dreaming
공식 코드Neuromorphic dreaming: A pathway to efficient learning in artificial agents
https://github.com/boschresearch/dist-mbrl
공식 코드https://github.com/boschresearch/ube-mbrl
공식 코드https://github.com/cagatayyildiz/oderl
공식 코드https://github.com/cair/deep-warehouse
공식 코드Towards Model-based Reinforcement Learning for Industry-near Environments
https://github.com/cap-ntu/baconian-project
공식 코드Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning
https://github.com/catalin-2002/simudice
공식 코드SimuDICE: Offline Policy Optimization Through World Model Updates and DICE Estimation
https://github.com/causal-rl-anonymous/causal-rl
공식 코드Causal Reinforcement Learning using Observational and Interventional Data
https://github.com/cav-research-lab/predictive-model-delay-correction
공식 코드https://github.com/cerenaut/wmar
공식 코드Augmenting Replay in World Models for Continual Reinforcement Learning
https://github.com/chandar-lab/LoCA
공식 코드The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
https://github.com/chandar-lab/LoCA2
공식 코드Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
https://github.com/chandar-lab/Recall2Imagine
공식 코드https://github.com/changmin-yu/ccwm_code
공식 코드Learning State Representations via Retracing in Reinforcement Learning
https://github.com/chengyanggu/neuralpmp2024
공식 코드https://github.com/chrisgrimm/proper_value_equivalence
공식 코드https://github.com/cog-isa/schema-rl
공식 코드https://github.com/cr-gjx/ria
공식 코드https://github.com/cristianocapone/biodreaming
공식 코드Towards biologically plausible Dreaming and Planning in recurrent spiking networks
https://github.com/danielpalen/value_expansion
공식 코드Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
https://github.com/data-science-in-mechanical-engineering/infoprop
공식 코드https://github.com/david-friede/lddr
공식 코드https://github.com/dido1998/CausalMBRL
공식 코드Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
https://github.com/dnandha/mopac
공식 코드Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
https://github.com/dotchen/WorldOnRails
공식 코드https://github.com/dtak/mbrl-smdp-ode
공식 코드Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.