This thesis proposes a study towards the application of imitation learning (IL) algorithms Action Chunking Transformer (ACT) and diffusion policy as an autonomous surface vessel (ASV) path planner in complex marine environments. Rationale for conduct- ing this research are the ubiquitous limitations regarding fine tuning cost- and or reward functions of conventional state of the art algorithms for ASV navigation in complex environments. In this study we trained both algorithm types on data sources collected from a basic 2D-grid sim- ulator and a more realistic GAZEBO simulator. Subse- quently we evaluated both algorithm’s performances in each respective simulator in terms of the success rate for a standard navigation task. We found relatively high suc- cess rates for the 2D-grid simulator (0.98 for ACT and 0.53 for diffusion policy). For the GAZEBO simulator, we found poor performance for ACT (0.0 success rate) and for diffusion policy we could not establish the perfor- mance due to hardware limitations. For future work, the capabilities of both models could be investigated further by trying to bridge the gap between the simple 2D simula- tor and the GAZEBO simulator. Mainly the effect of task complexity and the quality and quantity of data used for training the models on the performances of the models can be investigated in these future work studies.