Papers with Code 아카이브
Model-based Reinforcement Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MVerghese/NN-Dynamics-Sparse-Reward
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/MindCode-4/code-8/tree/main/Neural-Network-based-Speech-Enhancement
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/MindSpore-scientific-2/code-5/tree/main/Integrated-Gradient
https://github.com/Olloxan/Pytorch-A2C
Imagination-Augmented Agents for Deep Reinforcement Learning
https://github.com/Qtsho/model-based-rl-navigation
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/Shunichi09/PythonLinearNonlinearControl
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/WilsonWangTHU/mbbl-metrpo
https://github.com/Xingyu-Lin/mbpo_pytorch
https://github.com/XueyingBai/Model-Based-Reinforcement-Learning-for-Online-Recommendation
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
https://github.com/ZhengXinyue/fuck_papers
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
https://github.com/adrialopezescoriza/demo3
TD-MPC2: Scalable, Robust World Models for Continuous Control
https://github.com/anagabandi/nn_dynamics
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/angelolovatto/raylab
https://github.com/anyboby/ConstrainedMBPO
https://github.com/aravind0706/neural-mpc
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/aravindsrinivas/neural-mpc
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/argmax-ai/drone-hardware
https://github.com/braincog-x/brain-cog
Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning
https://github.com/buoyancy99/diffusion-forcing
https://github.com/danielwillemsen/PendulumDemo
https://github.com/edantoledo/stoix
https://github.com/enpasos/muzero
Online and Offline Reinforcement Learning by Planning with a Learned Model
https://github.com/ermongroup/CalibratedModelBasedRL
https://github.com/facebookresearch/minihack
The Effectiveness of World Models for Continual Reinforcement Learning
https://github.com/facebookresearch/slbo
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
https://github.com/fusion-ml/trajectory-information-rl
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/gauthambekal93/hvac_continual_rl
https://github.com/github-jnauta/pytorch-pne
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/google/brain-tokyo-workshop
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
https://github.com/iclavera/learning_to_adapt
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/idthanm/env_build
https://github.com/jesbu1/carl
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/jingwu6/handful-of-trials-in-pytorch
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/jxu43/replication-mbpo
https://github.com/lucys0/HRL
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.