Description: Reinforcement Learning with Neural Networks is a technique that combines two fundamental areas of artificial intelligence: reinforcement learning (RL) and neural networks. In this approach, an agent learns to make decisions by interacting with an environment, receiving rewards or penalties based on its actions. Neural networks are used to approximate value functions or policies, allowing the agent to handle complex state and action spaces that would be unmanageable with traditional methods. This approach is particularly powerful because it enables agents to learn complex behaviors and adapt to dynamic situations. As the agent explores its environment, it adjusts its strategies based on the feedback received, allowing it to improve its performance over time. The combination of RL and neural networks has led to significant advances in areas such as robotics, video games, and system optimization, where real-time and adaptive decision-making is required. This approach has proven effective in solving problems involving uncertainty and variability, making it a valuable tool in the field of modern artificial intelligence.
History: Reinforcement learning has existed since the 1950s, but its combination with neural networks began to gain attention in the 1990s. An important milestone was Gerald Tesauro’s work in 1995, who used neural networks to play the board game ‘Backgammon’, achieving competitive results. However, it was in 2013 that the approach gained new momentum with the development of algorithms like Deep Q-Networks (DQN) by researchers at Google DeepMind, demonstrating that neural networks could learn to play Atari video games at a human level. Since then, the field has rapidly evolved, with advances in algorithms and architectures enabling applications in various areas.
Uses: Reinforcement learning with neural networks is used in a variety of applications, including robotics, where agents learn to perform complex tasks through interaction with their environment. It is also applied in video game development, where agents can learn to play and improve their performance. Other areas of use include system optimization, resource management, and real-time decision-making in dynamic environments.
Examples: A notable example is the use of DQN by Google DeepMind to play Atari video games, where the agent learned to play at a level comparable to that of humans. Another case is the development of robots that learn to manipulate objects in unstructured environments, such as those used in the manufacturing industry. Additionally, it has been used in recommendation systems, where algorithms learn to suggest products based on user interactions.
