  We report two methods for solving FBSDEs of path dependent types of high
dimensions. Specifically, we propose a deep learning framework for solving such
problems using path signatures as underlying features. Our two methods
(forward/backward) demonstrate comparable/better accuracy and efficiency
compared to the state of the art techniques. More importantly, we are able to
solve the problem of high dimension which is a limitation in the conventional
methods. We also provide convergence proof for both methods with the proof of
the backward methods in the Markovian case.
