Deep reinforcement learning is a subfield of machine learning that combines deep learning with reinforcement learning. In traditional reinforcement learning, the agent learns by interacting with the environment and receiving rewards or penalties for its actions. The agent’s goal is to learn a policy that maximizes its cumulative reward over time. In contrast, deep reinforcement learning uses neural networks to approximate the optimal policy, instead of directly computing it.
Deep reinforcement learning algorithms typically use deep neural networks as function approximators to learn a mapping between states and actions. These networks can handle high-dimensional input, making them well-suited for complex tasks such as playing games or controlling robots. In addition, deep reinforcement learning algorithms can learn from raw sensory input, eliminating the need for hand-crafted features.
One of the major challenges in deep reinforcement learning is the instability of the learning process. As the agent interacts with the environment and updates its policy, the distribution of the data it sees can shift, leading to catastrophic forgetting. To address this issue, techniques such as experience replay and target networks are used to stabilize the learning process.
Overall, deep reinforcement learning is a powerful approach to solving complex decision-making problems where traditional reinforcement learning approaches may not be sufficient.