Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation

Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation

Reinforcement learning has gained significant interest in modern industries for its advancements in tackling challenging control tasks compared to rule-based programs. However, the robustness aspect of this technique is still under development, limiting its widespread adoption. This problem has beco...

Full description

Bibliographic Details
Main Authors: Aidar Shakerimov, Tohid Alizadeh, Huseyin Atakan Varol
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Distributional shift problem
domain adaptation
domain randomization
reality gap
reinforcement learning
robustness
Online Access:https://ieeexplore.ieee.org/document/10343164/

Internet

https://ieeexplore.ieee.org/document/10343164/

Similar Items