论文标题
在层次强化学习方案中解释代理商的决策
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario
论文作者
论文摘要
强化学习是一种基于行为心理学的机器学习方法。它的重点是可以通过与环境互动来获取知识并学会执行新任务的学习代理。但是,当系统用户需要对代理执行的操作具有更多信息和可靠性时,就会发生问题。在这方面,可解释的强化学习试图通过方法向代理提供培训中的代理商,以解释其行为,以至于没有机器学习经验的用户可以理解代理商的行为。其中之一是基于内存的可解释的增强学习方法,用于使用情节内存来计算每个州行动对的成功概率。在这项工作中,我们建议在层次结构环境中使用基于内存的可解释的增强学习方法,该层次结构环境由子任务组成,首先需要解决,以解决更复杂的任务。最终目标是验证是否有可能向代理提供解释其在全球任务以及子任务中的动作的能力。获得的结果表明,可以在具有高级任务的分层环境中使用基于内存的方法,并计算成功的概率被用作解释代理行为的基础。
Reinforcement learning is a machine learning approach based on behavioral psychology. It is focused on learning agents that can acquire knowledge and learn to carry out new tasks by interacting with the environment. However, a problem occurs when reinforcement learning is used in critical contexts where the users of the system need to have more information and reliability for the actions executed by an agent. In this regard, explainable reinforcement learning seeks to provide to an agent in training with methods in order to explain its behavior in such a way that users with no experience in machine learning could understand the agent's behavior. One of these is the memory-based explainable reinforcement learning method that is used to compute probabilities of success for each state-action pair using an episodic memory. In this work, we propose to make use of the memory-based explainable reinforcement learning method in a hierarchical environment composed of sub-tasks that need to be first addressed to solve a more complex task. The end goal is to verify if it is possible to provide to the agent the ability to explain its actions in the global task as well as in the sub-tasks. The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.