Explain the concept of deep reinforcement learning and how it combines reinforcement learning with deep learning. Discuss its application in complex environments like video games or autonomous driving.

Deep reinforcement learning (DRL) integrates reinforcement learning (RL) with deep neural networks to handle high-dimensional state and action spaces. In DRL, a neural network approximates the policy or value function, enabling the agent to learn from raw sensory inputs, such as pixels in video games or camera images in autonomous driving. Applications in video games include training agents to play Atari games using Deep Q-Networks (DQNs), while in autonomous driving, DRL is used to learn complex driving policies that navigate dynamic environments safely.