论文标题
探索探索：在统一环境中比较与RL代理的儿童
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
论文作者
论文摘要
发育心理学的研究始终表明，儿童彻底有效地探索了世界，这种探索使他们能够学习。反过来，这种早期学习支持以后生活中更强大的概括和聪明的行为。尽管开发机器学习中探索方法的许多工作已经进行，但人工代理人尚未达到其人类同行设定的高标准。在这项工作中，我们建议使用DeepMind Lab（Beattie等，2016）作为直接比较儿童和代理行为并开发新探索技术的平台。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，并概述了许多开放研究问题，我们认为可以使用这种方法进行测试。
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.
