Reinforcement learning method that learns while interacting with the environment, relies heavily on the concept of state as the input to the policy and value function. In the task, the view of agent contains a lot of information, and it is difficult for the agent to learn to ignore the irrelevant information and focus on the key information. Inspired by recent work in attention models for computer vision, we present a role‐based attention model for reinforcement learning. The proposed model uses convolutional neural networks to generate soft attention maps, adding crucial role information in the task, forcing the agent to focus on important features and distinguish task‐related information. To validate the performance in complex problems, the proposed approach is evaluated in a challenging scenario, Football Academy in Google Research Football Environment, a newly released reinforcement learning environment with physics‐based three‐dimensional simulator. The experimental results demonstrate that agents using role‐based attention mechanism can perform better in football games.

中文翻译：

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基于角色的注意力在游戏的深度强化学习中

在与环境交互时学习的强化学习方法在很大程度上依赖于状态的概念，作为对策略和价值功能的输入。在任务中，座席视图包含大量信息，并且座席很难学会忽略无关信息并专注于关键信息。受近期计算机视觉注意力模型研究的启发，我们提出了一种基于角色的注意力模型，用于强化学习。所提出的模型使用卷积神经网络生成软注意力图，在任务中添加关键角色信息，迫使代理专注于重要特征并区分与任务相关的信息。为了验证在复杂问题中的表现，在具有挑战性的情况下，足球学院评估了所建议的方法在Google Research Football Environment中，这是一个新发布的强化学习环境，具有基于物理学的三维模拟器。实验结果表明，使用基于角色的注意力机制的特工在足球比赛中的表现更好。