## 2 Related Work
[PERSON] et al. proposed a new framework [22] for GPU acceleration of DBSCAN and designed two tree-based algorithms to update clustering information by fusing neighborhood search, which achieves a good performance on low-dimensional data but underperforms on high-dimensional data. The RP-DBSCAN algorithm [10] was proposed based on the Spark parallel framework. It solves the problem of load imbalance between data partitions to a large extent through the pseudo-random partitioning based on cells and greatly improves the speed of the algorithm compared with the ordinary parallel DBSCAN algorithm, but it is only suitable for processing spatial data. The DBSCAN-based algorithms have a high sensitivity to parameters, and the accuracy of clustering results usually depends on the clustering parameters chosen based on experience. Recently, a lot of work has been conducted on adaptive or automated methods to determine the optimal parameters of DBSCAN. Among them, the DSets-DBSCAN algorithm [23] is to find the optimal parameter of _Eps_ by fixing a _MinPts_ value, and therefore, it is not fully intelligent. The AA-DBSCAN algorithm [24] adopts a new tree structure based on a quadtree to define the density layer of the dataset, but it still needs to input the relevant parameters. The KANN-DBSCAN algorithm [25] is based on a cyclic iterative optimization strategy, which generates the candidate _Eps_ and _MinPts_ parameter sets according to the distribution characteristics of dataset. The algorithm has a long runtime and requires manual selection for the acquired optimal parameter sets. The MOGA-DBSCAN algorithm [26] selects the parameters based on the multi-objective genetic algorithm, but it still has problems such as a long running time and the need for parameter tuning when facing large-scale data. In recent years, deep reinforcement learning (DRL) [27] has been well applied in parameter optimization by virtue of its integration of the powerful comprehension ability of deep learning [28] in perceptual problems and the decision-making ability of reinforcement learning [29], which enables it to make more accurate decisions in complex environments [30]. For example, a DRL-based DBSCAN algorithm [31] uses the TD3 [32] as the core algorithm to build an adaptive DBSCAN parameter tuning framework. We denote this DRL-based algorithm as TD3-DBSCAN in this paper to distinguish our proposed algorithm. Since the TD3 algorithm involves many parameters that have an impact on the performance, such as the learning rate and the update frequency of target network, a large number of experiments and debugging are required, and the stability of the obtained optimal parameters is poor. The maximum entropy principle-based method tends to allow the intelligent agent to learn a random strategy that enables it to explore more behaviors in the scenarios with multiple optimal or sub-optimal behaviors. Therefore, the soft actor-critic (SAC) algorithm [33], a deep reinforcement learning algorithm based on the maximum entropy principle and the actor-critic framework, has the advantages of high stability, wide exploration space, and less influence by parameters. At the same time, DRL algorithms such as DQN [34] adopt the experience playback mechanism as a random sampling method with equal probability, which can ensure that each experience can be well sampled and learned by the agent. However, different experiences have different impacts on policy optimization, and the random sampling approach ignores the importance of high-value experiences. The prioritized experience replay (PER) [35] differentiates the importance of experience and prioritizes the sampling of high-value experience, which allows it to better optimize the strategy and improve the stability and efficiency of the algorithm.