## 3 Results, Discussion and Outlook
### Categories of Research Studies
#### 3.2.3 Methods of Training and Techniques
[PERSON] and [PERSON] [73] present a proof-of-concept for agent-based simulations in real maritime environments. Although more work is needed to integrate a full motion model with six degrees of freedom and overcome the limitations of continuous action space algorithms, the results of this study show promise for advancing autonomous ship navigation through a combination of deep reinforcement learning and supervised learning that is based on historical data and collision avoidance mechanisms. [PERSON] et al. [74] experimentally evaluate an algorithm for simulating ship motion with six degrees of freedom. It shows different capabilities compared to the existing methods and achieves satisfactory predictions in terms of waves, sway and yaw; while there are limitations in pitch and heave, the system demonstrates its effectiveness in virtual maritime learning and training and offers a cost-effective alternative to traditional ship maneuvering training with a potential application in maritime education. In their article, [PERSON] et al. [75] conclude that the rapid development of shipping technology poses a challenge for maritime education providers and necessitates an adaptation of competence requirements for professionals. Satakunta University of Applied Sciences is addressing this challenge with ISTLAB, a simulator-based laboratory, demonstrating a proactive approach to research and education that is aligned with the evolving landscape of smart shipping technology and anticipates future skill requirements in collaboration with international partners. [PERSON] [76] discusses a comprehensive framework to support navigation in autonomous ships and emphasizes the importance of integrating deep learning-based ship intelligence for decision-making. [PERSON] and [PERSON] [77] conclude that the maritime community, which includes both research and industry, is actively advancing the integration of unmanned systems, including autonomous ships, into maritime operations. Despite varying levels of control, ranging from remote control to near complete autonomy on prototype ships, the successful introduction of this technology must consider the safety concerns regarding equipment, cargo and seafarers. [PERSON] [78] presents the current advances and challenges in the field of autonomous ship technologies, highlighting the lack of a comprehensive framework that encompasses technical, human and legal aspects. He highlights the complicated dynamics of navigation coordination and emphasizes that autonomous ships must make decisions on their own, especially in collision avoidance, while understanding and responding to human-operated ships. The discussion further addresses the integration of machine learning into simulation environments for crew training and points to the need for more sophisticated, intelligent agent-based simulations to prepare for the evolving landscape of autonomous shipping. The future success of the shipping industry depends to a large extent on effectively addressing the challenges associated with autonomous shipping, such as training [79]. [PERSON] and [PERSON] project [80] that preparing for the age of autonomous navigation is a major challenge. The authors note that there is a gap in preparation and uncertainty in training and skills, and he emphasizes that traditional maritime skills are still needed and points to the complex task facing the IMO as it attempts to write regulations, education and training to address the absence of the human element. At present, there is no definitive clarity in theory and training as to what the future autonomous ship operators might be in terms of their responsibilities, competencies, etc. [81]. 2.4 Surveys and Studies of Collaboration between Industry Stakeholders, Educational Institutions and Training Providers  
Education must follow technological and social developments, both in terms of curricula and methods.