The Boltzmann distribution assumption in the skill distribution may have limitations in scenarios where the skill values are not well-separated or when the skill space is high-dimensional. In such cases, the Boltzmann distribution may struggle to effectively capture the relative importance of different skills. To address this limitation, we could explore alternative distributional forms that can better model the skill values, such as using a Gaussian distribution or a mixture of distributions. Additionally, incorporating a temperature parameter that adapts dynamically based on the local entropy could further enhance the exploration capabilities of the agent. By continuously adjusting the temperature, the agent can strike a balance between exploitation and exploration, leading to more efficient learning and goal achievement.