Modelagem Recompensa is a concept in aprendizado por reforço (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 exemplo, ao treinar um robô para navegar em um labirinto, em vez de recompensar o robô apenas quando ele sai do labirinto, ele pode receber pequenas recompensas por cada passo dado em direção à saída ou por fazer uma curva correta. Isso ajuda o agente a aprender a tarefa mais rapidamente e de forma mais eficaz, pois recebe feedback continuamente ao longo do processo.
Existem vários tipos de recompensas de modelagem, incluindo as baseadas em potencial modelagem de recompensa, 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 ótima of the agent; they should only guide it towards learning that policy de forma mais 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.