Deep neural networks (DNNs) are crucial in reinforcement learning (RL) because they provide the capability to approximate complex functions that represent the agent’s decision-making process. In RL, an agent typically needs to estimate the value of actions or policies, and deep neural networks help by modeling these value functions or directly mapping states to actions.
DNNs are particularly valuable in environments with large or continuous state spaces, such as video frames or sensor data, where traditional tabular methods fail. For example, in Deep Q-Learning, a DNN is used to approximate the Q-value function that represents the expected return for a given state-action pair. The neural network learns from the agent's experiences and adjusts the weights based on the Q-learning update rule.
In policy gradient methods, neural networks are used to model the policy directly, learning the parameters that maximize expected returns. By using DNNs, RL algorithms can scale to more complex environments, where conventional methods would be impractical.