自主智能Agent的主动学习研究
Investigating Enactive Learning for Autonomous Intelligent Agents
摘要：
认知的生成方法通常被认为是传统认知科学的一种可行的替代方法。生成认知将解释的焦点从施事的内部表征转移到与环境的直接感觉运动交互。在本文中，我们通过人工代理模拟的方式研究生成学习。我们比较了在迷宫环境中觅食任务的经典强化学习上操作的代理的执行。代理的特性进行了分析，在环境状态，目标和勘探/开采权衡的可访问性。我们证实，制定剂可以成功地与环境相互作用，并学会避免不利的相互作用，使用内在定义的目标。的enactive代理的性能被证明是有限的负担得起的行动的数量。
英文摘要：
The enactive approach to cognition is typically proposed as a viable alternative to traditional cognitive science. Enactive cognition displaces the explanatory focus from the internal representations of the agent to the direct sensorimotor interaction with its environment. In this paper, we investigate enactive learning through means of artificial agent simulations. We compare the performances of the enactive agent to an agent operating on classical reinforcement learning in foraging tasks within maze environments. The characteristics of the agents are analysed in terms of the accessibility of the environmental states, goals, and exploration/exploitation tradeoffs. We confirm that the enactive agent can successfully interact with its environment and learn to avoid unfavourable interactions using intrinsically defined goals. The performance of the enactive agent is shown to be limited by the number of affordable actions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
