SPRINQL : Sub-optimal demonstrations driven offline imitation learning
We focus on offline imitation learning (IL), which aims to mimic an expert's behavior using demonstrations without any interaction with the environment. One of the main challenges in offline IL is the limited support of expert demonstrations, which typically cover only a small fraction of the s...
Saved in:
Main Authors: | HOANG, Minh Huy, MAI, Tien, VARAKANTHAM, Pradeep |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9821 https://ink.library.smu.edu.sg/context/sis_research/article/10821/viewcontent/Neurips_2024___SPRINQL.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Imitate the good and avoid the bad: An incremental approach to safe reinforcement learning
by: HOANG, Minh Huy, et al.
Published: (2024) -
Mimicking to dominate: Imitation learning strategies for success in multiagent competitive games
by: BUI, The Viet, et al.
Published: (2024) -
Behavior imitation for manipulator control and grasping with deep reinforcement learning
by: Liu, Qiyuan
Published: (2024) -
Imitation learning from demonstration videos
by: Zeng, Jingbo
Published: (2024) -
Intelligent robot manipulation with deep learning
by: Tan, Jun Aun
Published: (2024)