Neural networks play a key role in reinforcement learning (RL) by approximating the decision-making function, typically in environments with complex or high-dimensional state and action spaces. In RL, an agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties. The neural network is used to model the agent’s policy or value function.
For example, in deep Q-learning, a neural network is used to approximate the Q-function, which helps the agent decide which actions to take based on the state it is in. In policy-gradient methods, neural networks are used to directly represent the policy.
Neural networks enable RL models to tackle challenging tasks, such as playing video games, robotics, and autonomous driving, by providing the necessary function approximation to handle complex, dynamic environments.