Deep reinforcement learning (DRL) algorithms combine the concepts of reinforcement learning (RL) and deep learning. In DRL, deep neural networks are used to approximate the value functions or policies in RL problems, allowing the agent to handle high-dimensional input spaces like images or continuous environments. DRL algorithms are designed to learn optimal policies or value functions through trial and error by interacting with the environment.
A common DRL approach is Deep Q-Networks (DQN), where a neural network is used to approximate the Q-values of actions in a given state. Another popular algorithm is Proximal Policy Optimization (PPO), which optimizes a policy using neural networks and aims to balance exploration and exploitation. These algorithms have been applied successfully to complex environments such as video games, robotics, and autonomous systems.
DRL algorithms require large amounts of training data and computational resources but are powerful tools for solving real-world, high-dimensional problems.