https://github.com/StoneT2000/trajectorytranslation
공식 코드Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Papers with Code 아카이브
Few-Shot Imitation Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/aielawady/relic
공식 코드ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI
https://github.com/frankzheng2022/prise
공식 코드PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
https://github.com/mpatacchiola/imujoco
공식 코드Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/premiertaco/premier-taco
공식 코드https://github.com/seongwoongcho/meta-controller
공식 코드Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
https://github.com/stepjam/TecNets
공식 코드Task-Embedded Control Networks for Few-Shot Imitation Learning
https://github.com/MindCode-4/code-11/tree/main/comparing-the-efficacy
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/MindCode-4/code-6/tree/main/comparing-the-efficacy
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/MindSpore-scientific/code-3/tree/main/comparing-the-efficacy
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/lihenglin/bridge_training_code
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
https://github.com/naruya/tecnets-pytorch
Task-Embedded Control Networks for Few-Shot Imitation Learning
https://github.com/stepjam/PyRep
Task-Embedded Control Networks for Few-Shot Imitation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.