The “Deep” in Reinforcement Learning
Deep Reinforcement Learning incorporates deep neural networks into the framework of Reinforcement Learning. This integration allows for more complex and high-dimensional representations of states and actions, enabling the agent to learn and make decisions in environments with large state spaces.
In traditional Reinforcement Learning approaches, such as Q-Learning, a tabular representation (Q-table) is used to store the values of state-action pairs. However, in Deep Reinforcement Learning, a deep neural network is employed to approximate the values of states or state-action pairs. This neural network, also known as a Deep Q-Network (DQN), takes in the state as input and outputs the corresponding Q-values, which represent the expected return for each action.
By utilizing deep neural networks, Deep Reinforcement Learning can handle more complex and diverse environments. The neural network learns to generalize from observed experiences and make predictions about unseen states or actions. This enables the agent to make informed decisions based on the learned knowledge, even in situations that were not explicitly encountered during training.
Deep Reinforcement Learning has achieved remarkable success in various domains, including robotics, game playing, natural language processing, and autonomous driving. The combination of Reinforcement Learning with deep neural networks allows for the exploration and exploitation of large-scale, high-dimensional problems, opening up new possibilities for intelligent decision-making systems.
