  Engineering design problems often involve large state and action spaces along
with highly sparse rewards. Since an exhaustive search of those spaces is not
feasible, humans utilize relevant domain knowledge to condense the search
space. Previously, deep learning agents (DLAgents) were introduced to use
visual imitation learning to model design domain knowledge. This note builds on
DLAgents and integrates them with one-step lookahead search to develop
goal-directed agents capable of enhancing learned strategies for sequentially
generating designs. Goal-directed DLAgents can employ human strategies learned
from data along with optimizing an objective function. The visual imitation
network from DLAgents is composed of a convolutional encoder-decoder network,
acting as a rough planning step that is agnostic to feedback. Meanwhile, the
lookahead search identifies the fine-tuned design action guided by an
objective. These design agents are trained on an unconstrained truss design
problem that is modeled as a sequential, action-based configuration design
problem. The agents are then evaluated on two versions of the problem: the
original version used for training and an unseen constrained version with an
obstructed construction space. The goal-directed agents outperform the human
designers used to train the network as well as the previous objective-agnostic
versions of the agent in both scenarios. This illustrates a design agent
framework that can efficiently use feedback to not only enhance learned design
strategies but also adapt to unseen design problems.
