https://github.com/KarlXing/LUSR
공식 코드Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
Papers with Code 아카이브
Transfer 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/Mohammadamin-Barekatain/multipolar
공식 코드https://github.com/SilentEchoes77/hybrid-transfer-rl
공식 코드Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
https://github.com/adaptive-rl/adarl-code
공식 코드AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
https://github.com/cuijiaxun/MITC-Project
공식 코드Scalable Multiagent Driving Policies For Reducing Traffic Congestion
https://github.com/ondrejba/action_priors
공식 코드https://github.com/saodem74/Transfer-Learning-in-Reinforcement-Learning
공식 코드Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
https://github.com/seba-1511/shapechanger
공식 코드https://github.com/shamanez/VUSFA-Variational-Universal-Successor-Features-Approximator
공식 코드https://github.com/taochenshh/hcp
공식 코드Hardware Conditioned Policies for Multi-Robot Transfer Learning
https://github.com/yaserkl/TransferRL
공식 코드https://github.com/AcutronicRobotics/gym-gazebo2
gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo
https://github.com/andris955/diplomaterv
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
https://github.com/aravindsrinivas/upn
https://github.com/eparisotto/ActorMimic
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
https://github.com/omron-sinicx/multipolar
https://github.com/shamanez/masters-work-target-driven-visual-navigation
https://github.com/tophatraptor/si-transfer
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.