MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy
Zhanpeng He,Matei Ciocarlie,Zhanpeng He,Matei Ciocarlie
We introduce MORPH, a method for co-optimization of hardware design parameters and control policies in simulation using reinforcement learning. Like most co-optimization methods, MORPH relies on a model of the hardware being optimized, usually simulated based on the laws of physics. However, such a model is often difficult to integrate into an effective optimization routine. To address this, we in...
