Neural networks play a central role in reinforcement learning (RL) by enabling agents to learn complex behaviors in environments with high-dimensional state spaces. In RL, an agent interacts with an environment, takes actions, and receives rewards or penalties to optimize its decision-making strategy. Traditional RL methods, like tabular Q-learning, struggle when the state space is large or continuous (e.g., pixels in a video game or sensor data in robotics). Neural networks address this by approximating functions such as the policy (which action to take) or the value function (expected long-term reward), allowing the agent to generalize from limited data and handle raw, unstructured inputs effectively. For example, a neural network can process visual input from a game screen and map it to actions without manual feature engineering.
A key application of neural networks in RL is in value-based methods like Deep Q-Networks (DQN). In DQN, a neural network approximates the Q-value function, which estimates the expected reward for taking a specific action in a given state. This approach was famously used to train agents to play Atari games directly from pixel data. The network, often a convolutional neural network (CNN), processes raw frames, extracts spatial features, and outputs Q-values for each possible action. Policy-based methods, such as Proximal Policy Optimization (PPO), use neural networks to directly parameterize the policy, outputting probabilities for each action. Actor-critic architectures combine both approaches: one network (the actor) decides actions, while another (the critic) evaluates the quality of those actions, enabling more stable training in complex environments like robotics or autonomous driving simulations.
However, integrating neural networks into RL introduces challenges. Training stability is a major concern, as small changes in the network’s predictions can lead to large shifts in the agent’s behavior. Techniques like experience replay (storing past transitions to decorrelate training data) and target networks (using a separate network to stabilize Q-value targets) are often necessary. Exploration vs. exploitation—balancing trying new actions versus sticking to known rewards—is another hurdle, addressed through methods like entropy regularization or epsilon-greedy strategies. Additionally, hyperparameter tuning (e.g., learning rates, discount factors) and computational costs (training in simulated environments for millions of steps) require careful optimization. Despite these challenges, neural networks remain indispensable for scaling RL to real-world problems, from game AI to industrial control systems.