Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies
Hanbit Oh,Hikaru Sasaki,Brendan Michael,Takamitsu Matsubara,Hanbit Oh,Hikaru Sasaki,Brendan Michael,Takamitsu Matsubara
Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an over-reliance on replicating expert actions induces inflexible and unstable policies, leading to poor generalizability in an application. To address the problem, this paper presents the first imitation learning framework...
