论文标题
通用学习代理的神经进化框架
Neuro-evolutionary Frameworks for Generalized Learning Agents
论文作者
论文摘要
深度学习和深度强化学习的最新成功已牢固地确立了他们作为最先进的人工学习技术的地位。但是，这些方法的长期缺点，例如它们的样本效率差和有限的概括能力表明，需要重新思考此类系统的设计和部署方式。在本文中，我们强调了这些学习系统的使用如何以及进化算法的特定变化如何导致出现独特特征，例如自动化各种期望的行为和有用的行为探讨集合。这可以为学习以广义和持续的方式学习的方式铺平道路，并且与环境的相互作用最少。我们讨论了此类神经进化框架的预期改善以及相关的挑战，以及其在许多研究领域的应用潜力。
The recent successes of deep learning and deep reinforcement learning have firmly established their statuses as state-of-the-art artificial learning techniques. However, longstanding drawbacks of these approaches, such as their poor sample efficiencies and limited generalization capabilities point to a need for re-thinking the way such systems are designed and deployed. In this paper, we emphasize how the use of these learning systems, in conjunction with a specific variation of evolutionary algorithms could lead to the emergence of unique characteristics such as the automated acquisition of a variety of desirable behaviors and useful sets of behavior priors. This could pave the way for learning to occur in a generalized and continual manner, with minimal interactions with the environment. We discuss the anticipated improvements from such neuro-evolutionary frameworks, along with the associated challenges, as well as its potential for application to a number of research areas.
