A rede target is a type of rede neural commonly used in the field of aprendizado por reforço, 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 seleção de ações.
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 função 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.
A rede target é tipicamente uma cópia da rede principal (frequentemente chamada de rede online), mas é atualizada com menos frequência. Durante o treinamento, a rede online é usada para selecionar ações e gerar valores Q (estimativas de valor), enquanto a rede target é usada para calcular os valores Q-alvo para atualizar a rede online. Isso significa que a rede target fornece um ponto de referência estável para as atualizações, reduzindo o risco de mudanças drásticas causadas por flutuações nas estimativas de 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 ótima de forma mais eficaz.
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.