Description: The architecture of a reinforcement learning agent refers to the design and structure that enable an agent to interact with its environment, learn from experiences, and make decisions based on rewards and punishments. In this context, an agent is a system that perceives its environment through sensors and acts upon it through actuators. The architecture of these agents includes key components such as the value function, which evaluates the quality of actions based on expected rewards, and the policy, which defines how the agent chooses its actions in different states. Additionally, agents can incorporate exploration and exploitation techniques to balance the search for new strategies and the optimization of known ones. This architecture is fundamental to reinforcement learning, as it allows agents to adapt and improve their performance over time, learning from past interactions. The flexibility and generalization capability of these agents make them applicable in a variety of domains, from gaming to robotics and recommendation systems, where decision-making in dynamic environments is crucial.
History: The concept of reinforcement learning dates back to the 1950s, with early work in behavioral psychology inspiring computational models. However, it was in the 1980s and 1990s that reinforcement learning was formalized as a field of study in artificial intelligence, with algorithms like Q-learning and the development of Markov theory. Starting in 2010, advances in computational power and access to large volumes of data propelled the use of more complex architectures, such as deep neural networks, in reinforcement learning, leading to significant achievements in various areas.
Uses: The architecture of reinforcement learning agents is used in various applications, including video game development, where agents can learn to play and improve their performance. It is also applied in robotics, allowing robots to learn to perform complex tasks through interaction with their environment. Other areas include recommendation systems, optimization of processes, and autonomous vehicles, where real-time decision-making is essential.
Examples: A notable example of reinforcement learning agent architecture is AlphaGo, developed by DeepMind, which used deep neural networks to learn to play Go at a level surpassing human capabilities. Another example is the use of agents in simulation environments to train robots in tasks such as object manipulation or navigation in unknown environments.
