  Global routing has been a historically challenging problem in electronic
circuit design, where the challenge is to connect a large and arbitrary number
of circuit components with wires without violating the design rules for the
printed circuit boards or integrated circuits. Similar routing problems also
exist in the design of complex hydraulic systems, pipe systems and logistic
networks. Existing solutions typically consist of greedy algorithms and
hard-coded heuristics. As such, existing approaches suffer from a lack of model
flexibility and non-optimum solutions. As an alternative approach, this work
presents a deep reinforcement learning method for solving the global routing
problem in a simulated environment. At the heart of the proposed method is deep
reinforcement learning that enables an agent to produce an optimal policy for
routing based on the variety of problems it is presented with leveraging the
conjoint optimization mechanism of deep reinforcement learning. Conjoint
optimization mechanism is explained and demonstrated in details; the best
network structure and the parameters of the learned model are explored. Based
on the fine-tuned model, routing solutions and rewards are presented and
analyzed. The results indicate that the approach can outperform the benchmark
method of a sequential A* method, suggesting a promising potential for deep
reinforcement learning for global routing and other routing or path planning
problems in general. Another major contribution of this work is the development
of a global routing problem sets generator with the ability to generate
parameterized global routing problem sets with different size and constraints,
enabling evaluation of different routing algorithms and the generation of
training datasets for future data-driven routing approaches.
