Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn. In turn, this early learning supports more robust generalization and intelligent behavior later in life. While much work has gone into developing methods for exploration in machine learning, artificial agents have not yet reached the high standard set by their human counterparts. In this work we propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly compare child and agent behaviors and to develop new exploration techniques. We outline two ongoing experiments to demonstrate the effectiveness of a direct comparison, and outline a number of open research questions that we believe can be tested using this methodology.
翻译:发展心理学的研究一贯表明,儿童对世界进行彻底和高效的探索,这种探索使他们得以学习。反过来,这种早期学习支持了在生命的后期更加有力的概括化和智能行为。虽然在开发机器学习的探索方法方面做了大量工作,但人造剂尚未达到其人类同行设定的高标准。在这项工作中,我们提议使用DeepMind实验室(Beattie等人,2016年)作为直接比较儿童和代理人行为的平台,并开发新的探索技术。我们概述了两个正在进行的实验,以证明直接比较的有效性,并概述了我们认为可以用这种方法测试的一些公开研究问题。