In object localization, methods based on a top-down search strategy that focus on learning a policy have been widely researched. The performance of these methods relies heavily on the policy in question. This paper proposes a deep ${Q}$ -network (DQN) that employs a multitask learning method to localize class-specific objects. This DQN agent consists of two parts, an action executor part and a terminal part. The action executor determines the action that the agent should perform and the terminal decides whether the agent has detected the target object. By taking advantage of the capability of feature learning in a multitask method, our method combines these two parts by sharing hidden layers and trains the agent using multitask learning. A detection dataset from the PASCAL visual object classes challenge 2007 was used to evaluate the proposed method, and the results show that it can achieve higher average precision with fewer search steps than similar methods.

中文翻译：

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使用深度强化学习进行目标定位的多任务学习

在对象定位中，基于自顶向下搜索策略的方法被广泛研究，该策略专注于学习策略。这些方法的性能在很大程度上取决于所讨论的策略。本文提出了一种深度 ${Q}$ -network (DQN)，它采用多任务学习方法来定位特定于类的对象。这个 DQN 代理由两部分组成，一个动作执行器部分和一个终端部分。动作执行器决定代理应该执行的动作，终端决定代理是否检测到目标对象。通过利用多任务方法中特征学习的能力，我们的方法通过共享隐藏层将这两部分结合起来，并使用多任务学习训练代理。