Deep neural networks (DNNs) enhance reinforcement learning (RL) by enabling agents to handle complex, high-dimensional environments that traditional RL methods struggle with. In RL, an agent learns to make decisions by interacting with an environment and receiving rewards. Classical approaches, like Q-learning, rely on tables or simple functions to represent policies or value estimates. However, these methods fail when states or actions are too numerous (e.g., in image-based environments). DNNs address this by approximating policies or value functions, allowing agents to generalize from limited data and operate in environments with vast state spaces. For example, Deep Q-Networks (DQN) use convolutional neural networks to process raw pixel inputs in games like Atari, replacing tabular Q-value storage with a neural network that predicts action values directly.
DNNs also enable RL agents to learn abstract representations of states and actions, which is critical for tasks requiring long-term planning. For instance, AlphaGo combines DNNs with Monte Carlo Tree Search to evaluate board positions and predict moves in Go, a game with more possible states than atoms in the universe. The neural network learns spatial patterns and strategic concepts from data, which guide the search algorithm. Similarly, in robotics, DNNs process sensor data (e.g., lidar or camera feeds) to map raw inputs to actions like motor control, bypassing handcrafted feature engineering. By compressing high-dimensional inputs into lower-dimensional embeddings, DNNs reduce the complexity of the decision-making process, making it feasible to train agents in realistic scenarios.
However, integrating DNNs into RL introduces challenges. Training stability is a key issue: neural networks can overfit to recent experiences or diverge due to feedback loops. Techniques like experience replay (storing past transitions in a buffer) and target networks (using delayed copies of the network to stabilize Q-value targets) mitigate these problems, as seen in DQN. Policy gradient methods, such as Proximal Policy Optimization (PPO), use DNNs to directly optimize policies while constraining updates to avoid drastic changes. These approaches balance exploration and exploitation, allowing agents to learn robust strategies. While DNNs add computational costs and hyperparameter tuning overhead, their ability to scale RL to real-world problems makes them indispensable in modern implementations.