Modelado Recompensa is a concept in aprendizaje por refuerzo (RL) that involves modifying the reward structure provided to an agent in order to promote desired behaviors and improve learning efficiency. Rather than offering a single reward for achieving a specific goal, shaping reward techniques break down complex tasks into manageable components, rewarding incremental progress towards the final objective.
In traditional reinforcement learning, an agent receives a reward signal only when it reaches a goal state. However, this can lead to inefficiencies, especially in environments with long or complex sequences of actions required to achieve that goal. Shaping reward addresses this issue by providing intermediate rewards based on the agent’s actions or states that are closer to the final goal.
Por ejemplo, en el entrenamiento de un robot para navegar por un laberinto, en lugar de recompensar solo cuando el robot sale del laberinto, podría recibir pequeñas recompensas por cada paso hacia la salida o por tomar un giro correcto. Esto ayuda al agente a aprender la tarea más rápidamente y de manera más efectiva, ya que recibe retroalimentación continuamente durante el proceso.
Hay varios tipos de recompensas de modelado, incluyendo las basadas en potencial modelación de recompensas, where the rewards are based on a potential function that predicts future rewards. The key is that these shaping rewards should not alter the política óptima of the agent; they should only guide it towards learning that policy de manera más eficiente.
Overall, shaping reward is an essential technique in designing reinforcement learning systems, as it helps enhance learning speed, improves agent performance, and enables the handling of more complex tasks.