## 1 Introduction and Related Work
In the past, different neural network architectures have been explored for motor-drive control system applications. Recently, reinforcement learning has emerged as one of the most promising approaches for parameter estimation and as a substitute for controllers. RL techniques develop a control policy by learning through trial and error without the need for supervised data labeling. The algorithm relies on a reward function to guide the learning process. This allows for the continuous improvement of control policies based on feedback, showcasing the evolving landscape of ML applications in motor drive monitoring and control. The reinforcement learning (RL)-based method is another promising data-driven technique explored in the field of the control of electric motor drives. An RL-enabled drive controller learns a control policy in a trial-and-error manner from the actual motor environment, thus avoiding the supervision of each data sample. The algorithm requires a reward function to receive the reward signals throughout the learning process. Thus, the control policy could be improved continuously based on the measurement feedback. Additionally, several other physical nonlinearities and parasitic influences from other drive system components can be adequately captured in the reinforced-based learning environment [30; 31]. Although the learning process can be performed offline, the trained RL agent can connect to a control interface in the controller making it suitable for real-time implementation without demanding a drastic change in the embedded hardware [32; 33]. Many exemplary works have been conducted that demonstrate its superiority in performance over a classic controller system. [PERSON] et al. [34] worked on an advanced deep reinforcement learning control with a dual Q learning approach for improving the performance of the current controller of a PMSM drive. The basis of the proposed controller is to learn the optimal action for the actor from the optimal voltage. A twin-delayed deep deterministic-policy gradient (TD3) reinforcement-learning-based parametrization method is proposed by the authors in [35] to parametrize the thermal model for an induction traction motor. The RL framework includes the TD3 agent, a reward function, observations, and actions taken by the agent. Observations represent the data that the RL agent collected from the parameterized thermal model, which includes direct measurements such as motor speed (MS) and torque (MT). The RL agent evaluated the policies for the identification of optimized parameters. Indirect measurement data such as stator and rotor temperatures and model outputs were utilized for reward calculation. The reward function produced a value indicating how effective the action is and guided the RL agent toward achieving its goal. This present work extends further to the applicability of multi-Agent reinforcement learning (MARL)-based framework comprising a set of RL agents such as soft actor-critic (SAC), and deep deterministic policy gradient (DDPG) besides the twin delayed deep deterministic policy gradient (TD3) for parameterizing the thermal model. Both measured and numerically estimated temperature data in several driving cycles are utilized to enhance the robustness of the parametrization and online adaptability. The structure of this paper is outlined as follows: a background and related work is presented in Section 1. Section 2 introduces the multiple-agents reinforcement learning framework developed for the optimization of the thermal model. Details regarding the dataset and the training methodology are also discussed in Section 2. Section 3 presents and analyzes the outcomes of the thermal model parameterization. Comparison of the proposed technique with the prior one is given in Section 4. Finally, Section 5 offers concluding thoughts and directions for future research.