Reinforcement learning (RL) uses deep neural networks (DNNs) to approximate complex functions that represent the agent's strategy or value assessments in a given environment. In traditional reinforcement learning, an agent learns by interacting with its environment, receiving rewards or penalties based on its actions. However, environments can be highly complex, making it challenging for the agent to learn effective strategies using simpler models. Deep neural networks provide the ability to handle high-dimensional input data, such as images, allowing the agent to learn from raw sensory inputs and make better decisions.
One common approach in reinforcement learning that utilizes deep neural networks is Deep Q-Learning (DQN). In this method, a neural network is used to approximate the Q-value function, which estimates the expected return or reward for taking certain actions in specific states. The DQN takes state information as input, processes it through its layers, and outputs Q-values for all possible actions. During training, the agent explores different actions, collects experience tuples (state, action, reward, next state), and periodically updates the neural network to improve the policy. For example, deep reinforcement learning has been successfully applied in games like Atari and Go, where the agent learns to play at superhuman levels by analyzing many thousands of game states.
Another approach is Policy Gradient methods, where deep neural networks directly represent the policy—that is, the strategy that the agent uses to decide its actions. These methods adjust the neural network's parameters to maximize the expected reward based on actions taken in specific states. An example of this is the Proximal Policy Optimization (PPO) algorithm, which strikes a balance between exploration and exploitation. In summary, deep neural networks enhance the efficiency and effectiveness of reinforcement learning by enabling more sophisticated function approximation, leading to better performance in complex environments.