Neural networks in deep reinforcement learning (DRL) are primarily used to approximate complex functions that map states to actions or predict future rewards. In traditional reinforcement learning, agents learn policies or value functions through tables or simpler models, but these approaches struggle with high-dimensional data like images or sensor inputs. Neural networks excel here by processing raw inputs (e.g., pixels from a game screen) and learning abstract representations, enabling agents to handle tasks that were previously infeasible. For example, in Deep Q-Networks (DQN), a neural network estimates the Q-values (expected rewards) for all possible actions in a given state, allowing the agent to choose optimal actions even in environments with vast state spaces, such as playing Atari games.
Another critical use of neural networks in DRL is to handle environments with partial observability or sequential decision-making. Recurrent Neural Networks (RNNs) or Transformer-based architectures can capture temporal dependencies, which is essential for tasks where the agent must remember past states to make informed decisions. For instance, in robotics, a robot navigating a dynamic environment might use an RNN-based policy to process a sequence of sensor readings and adjust its path in real time. Similarly, in multi-agent systems, neural networks can model interactions between agents by learning joint policies or communication protocols, as seen in collaborative games like StarCraft II, where agents coordinate using shared network architectures.
Finally, neural networks enable generalization across states and tasks, which is vital for scalability. Instead of memorizing specific state-action pairs, a trained network can interpolate between similar states, making it adaptable to unseen scenarios. This capability is leveraged in model-based DRL, where networks predict environment dynamics (e.g., how a robot’s movement affects its position) to plan ahead. For example, AlphaGo uses neural networks to evaluate board positions and predict opponent moves, combining these predictions with Monte Carlo Tree Search for long-term strategy. Developers often use frameworks like TensorFlow or PyTorch to implement these networks, balancing exploration (trying new actions) and exploitation (using known strategies) through techniques like experience replay or policy gradient methods.