SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

arXiv:2403.09110v1 Announce Type: new
Abstract: Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks often results in an uninterpretable, black-box policy …

abstract algorithms arxiv control cs.lg cs.sy dynamics eess.sy environments flow fusion however math.ds math.oc object reactor reinforcement reinforcement learning tokamak type