## 1 Introduction
In recent years, there has been a growing interest in developing platforms that enable the evaluation of multi-agent reinforcement learning and general AI approaches. One such platform is SMART, introduced by [29], an open-source system designed for multi-robot reinforcement learning (MRRL). SMART integrated a simulation environment and a real-world multi-robot system to evaluate AI approaches. In a similar vein, the Arcade Learning Environment (ALE) was proposed by [30] as a platform for evaluating domain-independent AI. The ALE provided access to hundreds of Atari 2600 game environments, offering a rigorous testbed for comparing various AI techniques. The platform also includes publicly available benchmark agents and software for further research and development. However, these approaches require specialized hardware or resources, limiting accessibility for those with standard PC setups to conduct research from home. Sampling efficiency and safety issues limit robot control in the real world. One solution is to train the robot control policy in a simulation environment and transfer it to the real world. However, policies trained in simulations often perform poorly in the real world due to imperfect modeling of reality in simulators [31]. The authors of [32] developed a training procedure, a set of actions available to the robot, a suitable state representation, and a reward function. The setup was evaluated using a simulated real-time environment. The authors compared a reference setup, different goal-oriented exploration strategies, and two different robot kinematics (holonomic and differential). With dynamic obstacles, the robot was able to reach the desired goal in 93% of the episodes. The task scenario was inspired by an indoor logistics setting, where one robot is situated in an environment with multiple obstacles and should execute a transport order. The robot's goal was to travel to designated positions in the least amount of time, without colliding with obstacles or with the walls of the environment. However, this was all done in the simulated setting. The authors of [33] designed an automated evaluation framework for Rapidly-Exploring Random Tree (RRT) frontier detector for indoor space navigation, which addressed the performance verification aspect. However, the method being verified was not of the RL category. The authors of [34] also designed a simulation framework for training the _Turtlebot_ robot agents. However, the framework was only designed for simulation purposes. These important works have set the stage for the examination of PPO in our study since the algorithm has a decent performance in robotic tasks and is easier to implement in mobile robots. The domain of mobile robot navigation presents a multitude of complexities, including navigating through cluttered spaces, avoiding obstacles, and making decisions in real time. To address these challenges, this study employs an automated evaluation framework tailored for Turtlebot robots, designed to rigorously assess the performance of RL techniques in a controlled environment. The investigation is anchored in an extensive analysis, focusing on the PPO algorithm. This technique is scrutinized in a discrete action space. The contributions of this paper are as follows:  
* We redesigned and implemented a personalized automated learning and evaluation framework based on an existing repository [34], allowing users to specify custom training and testing parameters for both simulated and physical robots (_Turtlebot_). This makes the platform adaptable and accessible to a wider range of users with limited resources. * To establish a robust benchmark for robotic research study, we design a simulated environment in Gazebo to train the agent. The training environment closely mimics typical indoor scenarios encountered by _Turtlebot_ robots with common obstacles such as walls and barriers, replicating real-world challenges.