The findings from this study can be extended to other robotic tasks, such as locomotion or multi-agent coordination, to guide the design of appropriate action spaces that facilitate efficient learning and transfer of policies. In the context of locomotion tasks, similar principles can be applied to design action spaces that prioritize smooth and stable movements, control higher-order derivatives for agility and robustness, and minimize tracking errors to improve transferability to real-world scenarios. For multi-agent coordination tasks, action spaces that enable effective collaboration and communication between agents, while considering constraints and safety measures, can be developed based on the insights gained from studying different action space characteristics. By tailoring action spaces to the specific requirements and dynamics of each task domain, researchers can enhance the performance and transfer capabilities of reinforcement learning policies in a variety of robotic applications.