https://github.com/utiasstars/visual-haptic-dynamics
공식 코드Learning Sequential Latent Variable Models from Multimodal Time Series Data
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/valarzz/dlpa
공식 코드Model-based Reinforcement Learning for Parameterized Action Spaces
https://github.com/valarzz/model-based-reinforcement-learning-for-parameterized-action-spaces
공식 코드Model-based Reinforcement Learning for Parameterized Action Spaces
https://github.com/vincentzhang/decentcem
공식 코드https://github.com/vmartinezalvarez/DyNODE
공식 코드DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control
https://github.com/vvanirudh/lamps-mbrl
공식 코드The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
https://github.com/wangzizhao/causaldynamicslearning
공식 코드Causal Dynamics Learning for Task-Independent State Abstraction
https://github.com/weipu-zhang/storm
공식 코드STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
https://github.com/wendongzh/continual_visual_control
공식 코드Predictive Experience Replay for Continual Visual Control and Forecasting
https://github.com/williambug/event_driven_rl
공식 코드Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
https://github.com/xiaonazhou/transdreamerv3
공식 코드https://github.com/xinshi-chen/GenerativeAdversarialUserModel
공식 코드Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
https://github.com/yannickycpan/reproducerl
공식 코드Understanding and Mitigating the Limitations of Prioritized Experience Replay
https://github.com/yasinsonmez/symmetry-cs285
공식 코드Exploiting Symmetry in Dynamics for Model-Based Reinforcement Learning with Asymmetric Rewards
https://github.com/yavaryeganeh/deep_aif
공식 코드Deep Active Inference Agents for Delayed and Long-Horizon Environments
https://github.com/yemam3/mod-rl-rcbf
공식 코드Safe Reinforcement Learning Using Robust Control Barrier Functions
https://github.com/yich7045/visuo-tactile-transformers-for-manipulation
공식 코드https://github.com/yingfan-bot/mbpsrl
공식 코드Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
https://github.com/younggyoseo/CaDM
공식 코드Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
https://github.com/younggyoseo/trajectory_mcl
공식 코드Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
https://github.com/yudasong/PCMLP
공식 코드PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
https://github.com/zchuning/latco
공식 코드Model-Based Reinforcement Learning via Latent-Space Collocation
https://github.com/zhejz/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/zhenghuazx/BayesianLRPolicySearch
공식 코드https://github.com/zhenghuazx/Policy-Optimization-in-Dynamic-Bayesian-Network
공식 코드Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes
https://github.com/zifanwu/plan-to-predict
공식 코드Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning
https://github.com/zihaosheng/traffic-expertise-rl
공식 코드https://github.com/Archibald-Lafraik/pipps
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
https://github.com/BY571/MBPO
https://github.com/Bam4d/Neural-Game-Engine
Neural Game Engine: Accurate learning of generalizable forward models from pixels
https://github.com/ByMic/PETS
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/Cejkis/I2A-Cejkis
Imagination-Augmented Agents for Deep Reinforcement Learning
https://github.com/DHDev0/Muzero-unplugged
Online and Offline Reinforcement Learning by Planning with a Learned Model
https://github.com/HumanCompatibleAI/deep-rlsp
https://github.com/JianGuanTHU/RecGAN
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
https://github.com/Learning-and-Intelligent-Systems/predicators_behavior
Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.