  Recently, a deep reinforcement learning method is proposed to solve
multiobjective optimization problem. In this method, the multiobjective
optimization problem is decomposed to a number of single-objective optimization
subproblems and all the subproblems are optimized in a collaborative manner.
Each subproblem is modeled with a pointer network and the model is trained with
reinforcement learning. However, when pointer network extracts the features of
an instance, it ignores the underlying structure information of the input
nodes. Thus, this paper proposes a multiobjective deep reinforcement learning
method using decomposition and attention model to solve multiobjective
optimization problem. In our method, each subproblem is solved by an attention
model, which can exploit the structure features as well as node features of
input nodes. The experiment results on multiobjective travelling salesman
problem show the proposed algorithm achieves better performance compared with
the previous method.
