  This paper is concerned with multi-view reinforcement learning (MVRL), which
allows for decision making when agents share common dynamics but adhere to
different observation models. We define the MVRL framework by extending
partially observable Markov decision processes (POMDPs) to support more than
one observation model and propose two solution methods through observation
augmentation and cross-view policy transfer. We empirically evaluate our method
and demonstrate its effectiveness in a variety of environments. Specifically,
we show reductions in sample complexities and computational time for acquiring
policies that handle multi-view environments.
