###### Abstract
The car learning to act (CARLA), which supported imitation learning and reinforcement learning (RL), was applied for training and evaluating AD [13]. A simulation of urban mobility (SUMO), which is an open-source simulator, can model various scenarios for a large area [14]. The recently developed Flow tool is an open-source framework that connects a simulation environment (e.g., SUMO, AIMSUM) and an RL library. The Flow tool was implemented for the AD in a mixed-traffic environment [15]. Thus, the fusion of SUMO and Flow represents an effective means to improve the AD domain. Importantly, many recent innovations in artificial intelligence (AI) have tried to enhance the development of the AD domain. For instance, the long short-term memory (LSTM) algorithm was applied to improve the navigation of AVs [16]. Moreover, the RL algorithm obtained an effective performance in improving complex policies in the AD field under an uncertain environment. The RL algorithm differs from unsupervised learning and supervised learning by optimizing the reward from the observation and state. The Markov decision process (MDP), which was suggested by [PERSON] [17], formalized sequential decision making for AD. [PERSON] et al. [18] used the multi-agent MDP in a mixed-traffic environment. However, the MDP paradigm is well-suited for full observation. For implementing AVs under dynamic conditions, the partially observable Markov decision process (POMDP) is a partial MDP observation [19]. This forecasts the surrounding participants for AD under mixed-traffic conditions. More recently, breakthroughs in deep neural networks (DNNs) have allowed us to enhance extraction features using multiple hidden layers. Based on this development, deep reinforcement learning (DRL), which integrates DNN and RL, can achieve effective decision making in dynamic environments. For instance, [PERSON] et al. [20] used the DRL method for adaptive traffic signal control. [PERSON] et al. [21] implemented the Double Deep Q-Network (DDQN) to achieve a better traffic signal policy. Therefore, it is necessary to study the AD in a mixed-traffic environment using the DRL method. Regarding comprehensive movement applications at non-signalized intersections, [PERSON] et al. [22] suggested the critical turning point method, which integrates a high-level candidate path generator through the POMDP algorithm. They considered incoming vehicle trajectories, such as right turns, left turns, and going straight ahead. [PERSON] et al. [23] suggested the DRL method for left-turn movements at a non-signalized intersection. They compared deep Q-learning (DQL) and double DQL based on the right-of-way rule. These results showed an improvement in the decision-making strategy, collision rate, and efficiency of traffic. [PERSON] and [PERSON] [24] proposed the use of a hybrid method that integrated POMDP and responsibility-sensitive safety algorithms. This method was applied to evaluate the efficiency of one automated vehicle (AV) turning left at a non-signalized intersection. Our results showed an improvement in the time performance and smoothing performance indices. However, the scenarios were simple with only one AV. As for hyperparameter turning studies, [PERSON] and [PERSON] [25; 26] applied a DRL to evaluate the efficiency of using AVs at a non-signalized intersection and in an urban network. We also suggested a set of proximal policy optimization (PPO) hyperparameters to improve the performance of DRL training. In particular, PPO with the adaptive Kullback-Leibler (KL) penalty was used for a non-signalized intersection, and clipped PPO was applied for an urban network. Our results presented improvements in the DRL training policy, smoothing velocity, mobility, and energy. Nevertheless, we only focused on the effectiveness of vehicles going straight ahead. In this work, we concentrate on the efficiency of comprehensive AD maneuvers (e.g., go straight ahead and left turn) at a non-signalized junction over different market penetration rates (MPRs) and real traffic volumes.