A new approach in reinforcement learning for fighting games allows agents to learn both the actions to take and the duration for which to execute them, enhancing adaptability in gameplay. This method shows promise in matching performance with traditional fixed frame skips while revealing that higher frame skip values can lead to more effective learning strategies against scripted opponents.