Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble. (arXiv:2206.00238v1 [cs.LG])

Inverse reinforcement learning (IRL) recovers the underlying reward function
from expert demonstrations. A generalizable reward function is even desired as
it captures the fundamental motivation of the expert. However, classical IRL
methods can only recover reward functions coupled with the training dynamics,
thus are hard to generalize to a changed environment. Previous
dynamics-agnostic reward learning methods have strict assumptions, such as that
the reward function has to be state-only. This work proposes a general approach
to learn transferable reward functions, …

arxiv dynamics ensemble learning