  Given an environment (e.g., a simulator) for evaluating samples in a
specified design space and a set of weighted evaluation metrics -- one can use
Theta-Resonance, a single-step Markov Decision Process (MDP), to train an
intelligent agent producing progressively more optimal samples. In
Theta-Resonance, a neural network consumes a constant input tensor and produces
a policy as a set of conditional probability density functions (PDFs) for
sampling each design dimension. We specialize existing policy gradient
algorithms in deep reinforcement learning (D-RL) in order to use evaluation
feedback (in terms of cost, penalty or reward) to update our policy network
with robust algorithmic stability and minimal design evaluations. We study
multiple neural architectures (for our policy network) within the context of a
simple SoC design space and propose a method of constructing synthetic space
exploration problems to compare and improve design space exploration (DSE)
algorithms. Although we only present categorical design spaces, we also outline
how to use Theta-Resonance in order to explore continuous and mixed
continuous-discrete design spaces.
