Karim highlights the stark differences between applying reinforcement learning in gaming versus real-world scenarios. While games offer clear reward systems and predefined environments, real-world applications present complex challenges such as defining reward functions and modeling intricate environments like railway networks. The speed and efficiency required for real-world simulations further complicate the process, making breakthroughs in gaming more common.
