Learning to Control Dynamic Systems with Automatic Quantization

Learning to control dynamic systems with unknown models is a challenging research problem. However, most previous work that learns qualitative control rules does not construct qualitative states | a proper partition of continuous state variables has to be designed by human users and given to the learning programs. We design a new learning method that learns appropriate qualitative state representation and the control rules simultaneously. Our method can aggressively partition the continuous state variables into ner, discrete ranges, until control rules based on these ranges are learned. As a case study, we apply our method to the benchmark control problem of cart-pole balancing (also known as the inverted pendulum). Experimental results show that not only does our method derive di erent partitions for the cart-pole systems with di erent parameters, but it also learns to control the systems for an extended period of time from random initial positions.