2 research outputs found
Inverse reinforcement learning in continuous time and space
This paper develops a data-driven inverse reinforcement learning technique
for a class of linear systems to estimate the cost function of an agent online,
using input-output measurements. A simultaneous state and parameter estimator
is utilized to facilitate output-feedback inverse reinforcement learning, and
cost function estimation is achieved up to multiplication by a constant
Online inverse reinforcement learning with limited data
This paper addresses the problem of online inverse reinforcement learning for
systems with limited data and uncertain dynamics. In the developed approach,
the state and control trajectories are recorded online by observing an agent
perform a task, and reward function estimation is performed in real-time using
a novel inverse reinforcement learning approach. Parameter estimation is
performed concurrently to help compensate for uncertainties in the agent's
dynamics. Data insufficiency is resolved by developing a data-driven update law
to estimate the optimal feedback controller. The estimated controller can then
be queried to artificially create additional data to drive reward function
estimation.Comment: 8 pages, 5 figures. arXiv admin note: text overlap with
arXiv:2003.0391