  Co-design involves simultaneously optimizing the controller and agents
physical design. Its inherent bi-level optimization formulation necessitates an
outer loop design optimization driven by an inner loop control optimization.
This can be challenging when the design space is large and each design
evaluation involves data-intensive reinforcement learning process for control
optimization. To improve the sample-efficiency we propose a
multi-fidelity-based design exploration strategy based on Hyperband where we
tie the controllers learnt across the design spaces through a universal policy
learner for warm-starting the subsequent controller learning problems. Further,
we recommend a particular way of traversing the Hyperband generated design
matrix that ensures that the stochasticity of the Hyperband is reduced the most
with the increasing warm starting effect of the universal policy learner as it
is strengthened with each new design evaluation. Experiments performed on a
wide range of agent design problems demonstrate the superiority of our method
compared to the baselines. Additionally, analysis of the optimized designs
shows interesting design alterations including design simplifications and
non-intuitive alterations that have emerged in the biological world.
