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/mahaitongdae/safe_exp_env
https://github.com/mehdimashayekhi/Some-RL-Implementation
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/nagaban2/nn_dynamics
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/natolambert/dynamicslearn
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/nicklashansen/tdmpc2
TD-MPC2: Scalable, Robust World Models for Continuous Control
https://github.com/opendilab/DI-engine
https://github.com/pikaju/pikaju
https://github.com/pokaxpoka/RAD_state_aug
https://github.com/pokaxpoka/rad_openaigym
https://github.com/probabilistic-ml/pirl
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
https://github.com/quanvuong/handful-of-trials-pytorch
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/ray-project/ray/tree/master/rllib
Model-Based Reinforcement Learning via Meta-Policy Optimization
https://github.com/rehoss/lbmpc_semimarkov
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/ronuchit/GLIB-AAAI-2021
https://github.com/roythuly/obac
TD-MPC2: Scalable, Robust World Models for Continuous Control
https://github.com/rvl-lab-utoronto/HyperCRL
Continual Model-Based Reinforcement Learning with Hypernetworks
https://github.com/sradicwebster/mbrl-lib
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/thomas-schillaci/SimPLe
https://github.com/ulissigroup/uncertainty_benchmarking
Accurate Uncertainties for Deep Learning Using Calibrated Regression
https://github.com/umd-huang-lab/reinforcement-learning-via-spectral-methods
Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning
https://github.com/wongongv/mbpo_fork
https://github.com/worldanon/learntopredict
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
https://github.com/wuxx1016/Reinforcement-Learning-in-Finance
https://github.com/zchuning/repo
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
https://github.com/zhejz/trafficbotsv1.5
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/zzyunzhi/asynch-mb
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.