论文标题
自主产生符号知识的期权发现
Option Discovery for Autonomous Generation of Symbolic Knowledge
论文作者
论文摘要
在这项工作中，我们介绍了一项经验研究，其中我们证明了开发能够自主探索实验场景的人造药物的可能性。在探索过程中，代理商能够发现和学习有趣的选项，允许在没有任何预先分配的目标的情况下与环境进行交互，然后抽象并重新利用获得的知识，以求解分配的事实的可能任务。我们在最近的文献中描述的所谓宝藏游戏领域中测试了该系统，我们从经验上证明，发现的选项可以在概率的符号计划模型（使用PPDDL语言）中抽象，从而使代理商可以生成符号计划来实现额外的目标。
In this work we present an empirical study where we demonstrate the possibility of developing an artificial agent that is capable to autonomously explore an experimental scenario. During the exploration, the agent is able to discover and learn interesting options allowing to interact with the environment without any pre-assigned goal, then abstract and re-use the acquired knowledge to solve possible tasks assigned ex-post. We test the system in the so-called Treasure Game domain described in the recent literature and we empirically demonstrate that the discovered options can be abstracted in an probabilistic symbolic planning model (using the PPDDL language), which allowed the agent to generate symbolic plans to achieve extrinsic goals.
