Reinforcement learning (RL) uses deep neural networks (DNNs) to approximate complex functions that are difficult to model manually, such as value functions (estimating future rewards) or policies (deciding actions). In traditional RL, methods like Q-learning or policy iteration rely on tables to store state-action values, but this becomes impractical in environments with high-dimensional inputs, like images or sensor data. DNNs solve this by acting as flexible function approximators, enabling RL agents to generalize across states and handle raw, unstructured data. This combination, called deep reinforcement learning (DRL), allows agents to learn directly from experience in complex environments without manual feature engineering.
A key example is Deep Q-Networks (DQN), which uses a DNN to estimate Q-values (action rewards) in Atari games. The network takes raw pixels as input and outputs Q-values for each possible action. DQN introduced techniques like experience replay (storing past transitions to break correlations in training data) and target networks (stabilizing learning by using a separate network to compute target Q-values). Similarly, policy gradient methods like Proximal Policy Optimization (PPO) use DNNs to represent the policy directly, outputting probabilities for actions. For instance, in robotics, a DNN might process joint angles and camera feeds to decide motor torques, learning through trial and error to maximize task completion rewards.
Practical implementation requires balancing exploration (trying new actions) and exploitation (using known strategies). DNNs in RL are sensitive to hyperparameters like learning rates and reward scaling. For example, training an agent to play a game might fail if rewards are sparse, requiring techniques like reward shaping (designing intermediate rewards) or curriculum learning (starting with simpler tasks). Frameworks like TensorFlow or PyTorch simplify building DNN architectures, while libraries like RLlib or Stable Baselines provide prebuilt DRL algorithms. Developers often face challenges like training instability, which can be mitigated by gradient clipping or normalization. Ultimately, DNNs enable RL to scale to real-world problems, but success depends on careful design of the network architecture, reward structure, and training process.