To extend the GEASD framework to handle continuous skill spaces, we can utilize techniques such as policy gradient methods or actor-critic architectures. By parameterizing the skill distribution as a continuous function, we can employ algorithms like Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC) to learn the optimal skill distribution. This approach allows for a more fine-grained control over the skill selection process, enabling the agent to smoothly transition between different skills in a continuous space. Additionally, we can incorporate techniques like entropy regularization to encourage exploration and prevent the distribution from collapsing to a single skill.