Deep reinforcement learning is a powerful combination of deep learning and reinforcement learning techniques. It has revolutionized the field of artificial intelligence and enabled significant advances in solving complex problems.

One of the key components of deep reinforcement learning is Deep Q-Networks (DQNs). DQNs are neural networks that approximate the action-value function, which maps states to the expected rewards of taking a particular action. By training DQNs on a large dataset of state-action pairs, an agent can learn to make optimal decisions in an environment.

Another technique used in deep reinforcement learning is policy gradients. Instead of trying to estimate the action-value function, policy gradients directly optimize the agent's policy, which is a function that maps states to probabilities of selecting each action. This approach can be especially effective in problems with large action spaces or continuous action spaces.

In summary, deep reinforcement learning combines the power of deep learning and reinforcement learning to tackle complex problems. By leveraging techniques like DQNs and policy gradients, agents can learn to make intelligent decisions in challenging environments.