###### Abstract
To obtain a better control performance from an AGC controller in large-scale interconnected power systems during an emergency situation, the dimension of many parameters (e.g., Q-value matrix **Q**, probability distribution matrix **P**, action set **A**, and state set **S**) of conventional reinforcement learning must be increased [25].However, increasing the dimension of these parameters can lead to the overflow of calculation memory, i.e., the curse of dimensionality [26]. Herein, a preventive strategy for AGC is considered to reduce emergency situation occurrences in a large-scale interconnected power system with a large continuous disturbance. A preventive strategy for AGC should have two major features, namely:  
1. The strategy should predict the next systemic state of the power system, and it should learn the feature of systemic frequency in the interconnected power system. That is to say, the preventive strategy should know whether the next state of the power system is an emergency state or a normal state, whereas conventional AGC without a preventive strategy cannot determine the next systemic state. 2. The strategy should provide an advanced generation command to the AGC unit with the prediction of the next systemic state, which includes the emergency state and normal state. Recently, [PERSON] and [PERSON] proposed an alternative to the deep neural network method for classification; this new method is known as deep forest or multi-grained cascade forest (gcForest) [27]. In [27], deep forest achieved a highly competitive performance in numerous classification experiments, such as image categorization, face recognition, music classification, hand movement recognition, sentiment classification, and the classification of low-dimensional data. The deep forest algorithm has been improved and further applied. For example, a discriminative deep forest was proposed in combination with Euclidean and Manhattan distances [28]; transductive transfer learning was applied to a convex quadratic optimization problem with linear constraints [29]; hyperspectral image classification was integrated into local binary patterns and a Gabor filter to extract local/global image features [30]; a distributed deep forest was applied to the automatic detection of cash-out fraud [31]; a Siamese deep forest was proposed for the prevention of overfitting, which takes place in neural networks when only limited training data are available [32]. Therefore, as an efficient algorithm for classification with low-dimensional data, deep forest can be applied to predict the next systemic state in a large-scale interconnected power system. To reduce occurrences of emergency situations and simultaneously mitigate the curse of dimensionality, deep forest reinforcement learning (DFRL) applied as a preventive strategy for AGC is proposed in this paper. The DFRL method consists of multiple subsidiary reinforcement learning and a deep forest. The multiple subsidiary reinforcement learning component of DFRL is applied to provide the generation command to the AGC unit of the large-scale interconnected power system, while the deep forest of DFRL is used to predict the next systemic state. Consequently, the major features of the DFRL method can be summarized as follows:  
1. Since reinforcement learning is applied to DFRL, DFRL can update its control strategy online. 2. The systemic states of a power system, including emergency states and normal states, can be predicted by the deep forest of DFRL using low-dimensional data. 3. Since both the Q-value matrix and the action set of reinforcement learning are split into those of an emergency situation and a normal situation, calculation memory is reduced. Thus, the curse of dimensionality is mitigated. This paper is organized as follows. The emergency state and automatic generation control are discussed in Section 2. Section 3 describes basic principle of deep forest reinforcement learning.