A red neuronal objetivo is a type of red neuronal commonly used in the field of aprendizaje por refuerzo, particularly in algorithms como Deep Q-Networks (DQN). Its primary purpose is to stabilize the training process by providing a more consistent set of value estimates for selección de acciones.
In reinforcement learning, agents learn to make decisions by interacting with an environment and receiving feedback in the form of rewards. However, directly updating the función de valor (which predicts the expected future rewards of actions) can lead to oscillations and instability during training. To mitigate this issue, the target network is introduced.
La red neuronal objetivo suele ser una copia de la red principal (a menudo llamada la red en línea), pero se actualiza con menos frecuencia. Durante el entrenamiento, la red en línea se usa para seleccionar acciones y generar valores Q (estimaciones de valor), mientras que la red objetivo se usa para calcular los valores Q objetivo para actualizar la red en línea. Esto significa que la red objetivo proporciona un punto de referencia estable para las actualizaciones, reduciendo el riesgo de cambios drásticos causados por fluctuaciones en las estimaciones de valores Q.
To maintain stability, the target network is updated periodically, often by copying the weights from the online network after a fixed number of training steps. This approach helps ensure that the learning process is more stable, allowing the agent to converge to an política óptima de manera más efectiva.
In summary, target networks play a crucial role in reinforcement learning by providing stability and consistency in value estimation, which is essential for the effective training of agents in dynamic environments.