The insights from the GEASD framework can be applied to a wide range of exploration-focused reinforcement learning problems beyond goal-conditioned tasks. For example, in robotic manipulation tasks, where the agent needs to learn complex manipulation skills, the adaptive skill distribution mechanism can help the agent explore different manipulation strategies efficiently. In autonomous driving scenarios, the agent can benefit from adaptive skill selection to navigate complex road environments and handle diverse driving conditions. Furthermore, in multi-agent systems, the GEASD framework can be used to enable agents to learn diverse strategies and adapt to changing environments collaboratively. Overall, the principles of adaptive skill distribution and deep exploration can be generalized to various reinforcement learning domains to improve learning efficiency and goal achievement.