We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert. In our setting, each perspective provides only limited information about the expert's behavior, and the learning agent must carefully select and combine information from different perspectives to achieve competitive performance. This setting is inspired by real-world imitation learning applications, e.g., in robotics, a robot might observe a human demonstrator via camera and receive information from different perspectives depending on the camera's position. We formalize the aforementioned active third-person imitation learning problem, theoretically analyze its characteristics, and propose a generative adversarial network-based active learning approach. Empirically, we demstrate that our proposed approach can effectively learn from expert demonstrations and explore the importance of different architectural choices for the learner's performance.

翻译：我们考虑第三人称模仿学习问题，并额外面临学习者必须选择观察专家的视角这一挑战。在我们的设定中，每个视角仅提供关于专家行为的有限信息，学习智能体必须仔细选择并整合来自不同视角的信息，以实现具有竞争力的性能。该设定受现实世界中的模仿学习应用启发，例如在机器人领域，机器人可能通过摄像头观察人类演示者，并根据摄像头的不同位置接收来自不同视角的信息。我们对上述主动第三人称模仿学习问题进行了形式化定义，理论分析了其特征，并提出了一种基于生成对抗网络的主动学习方法。实验表明，我们提出的方法能够有效从专家演示中学习，并探讨了不同架构选择对学习者性能的重要性。