Generative Control Inference
Chinese version: 生成式控制论的推理算法 - 知乎 (zhihu.com) In the previous post , I gave a peek into generative control, a new idea that can control some system by learning its intrinsic characteristics as a generative world model. It can avoid the over-shoot and over-expoloration problem from PID control and reinforcement learning. Rewrite the formuation a bit, we have \[[v,w,x] = g(z), \quad z \sim \mathbb{D},\] in which \(v\) is the collection of control objectives to be minimized, \(w\) contains the output control signals, and \(x\) represents the input signals that are thought to offer information to the control problem. During training, we are presented with a dataset of \([v,w,x]\) triplets, each representing the control objective \(v\) achieved under the output and input condition \([w,x]\). We have an algorithm that can produce \(g\), which is a generative model that can disambiguate the uncertainty of multiple acceptable \(v\), \(w\) or \(x\)'s, each under the condition of the other