Description: Deep Reinforcement Learning is a technique that combines deep learning and reinforcement learning to create intelligent agents capable of learning from high-dimensional sensory inputs. In this approach, deep neural networks are used to approximate value functions or policies, allowing agents to make decisions in complex and dynamic environments. Through interaction with the environment, the agent receives rewards or penalties, enabling it to adjust its behavior to maximize cumulative rewards over time. This learning process is based on exploration and exploitation, where the agent must balance the search for new strategies and the use of those it has already learned. The ability of neural networks to process large volumes of data and recognize complex patterns is fundamental in this context, as it allows agents to handle situations that would be difficult to manually encode. In summary, Deep Reinforcement Learning represents a significant advancement in the creation of autonomous systems that can adapt and learn from their environment, opening new possibilities in various technological applications.
History: The concept of Reinforcement Learning dates back to the 1950s, but its combination with Deep Learning began to gain attention in the 2010s. An important milestone was DeepMind’s work in 2013, where the DQN (Deep Q-Network) algorithm was introduced, using deep neural networks to play Atari video games, achieving performance superior to that of humans in several titles. This breakthrough demonstrated the viability of applying deep learning to reinforcement learning problems, marking the beginning of a new era in artificial intelligence.
Uses: Deep Reinforcement Learning is used in various applications, including robotics, gaming, recommendation systems, and process optimization. In robotics, it enables robots to learn to perform complex tasks through interaction with their environment. In the gaming realm, it has been used to develop agents that can compete with and surpass human players. It is also applied in optimization problems across various domains, including traffic systems and resource management in industrial settings.
Examples: A notable example of Deep Reinforcement Learning is AlphaGo, developed by DeepMind, which managed to defeat the world champion of Go in 2016. Another case is the use of this approach in robotics, where robots have been created that learn to walk or manipulate objects through experience. Additionally, it has been used in recommendation systems, where algorithms learn to suggest products based on user preferences.
