Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Karol Arndt,Murtaza Hazara,Ali Ghadirzadeh,Ville Kyrki,Karol Arndt,Murtaza Hazara,Ali Ghadirzadeh,Ville Kyrki
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware. In this work, we propose to address the problem of sim-to-real domain transfer by using meta learning to train a policy that can adapt to a variety of dynamic conditions, and using a task-specific trajectory generation model to pr...
