  The main concern of this study is to find the optimal design of truss
structures considering sizing and layout variables simultaneously. As compared
to purely sizing optimization problems, this problem is more challenging since
the two types of variables involved are fundamentally different in nature. In
this paper, a reinforcement learning method combining the update process and
Monte Carlo tree search called the update Monte Carlo tree search (UMCTS) for
sizing optimization problems is applied to solve combined sizing and layout
optimization for truss structures. This study proposes a novel update process
for nodal coordinates with two features. (1) The allowed range of each
coordinate varies in each round. (2) Accelerators for the number of entries in
the allowed range and iteration numbers are introduced to reduce the
computation time. Furthermore, nodal coordinates and member areas are
determined at the same time with only one search tree in each round. The
validation and efficiency of the UMCTS are tested on benchmark problems of
planar and spatial trusses with discrete sizing variables and continuous layout
variables. It is shown that the CPU time of the UMCTS is two times faster than
the branch and bound method. The numerical results demonstrate that the
proposed method stably achieves a better solution than other traditional
methods.
