论文标题
部分可观测时空混沌系统的无模型预测
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios
论文作者
论文摘要
在强化学习方案中使用交互式建议可以加快自主代理的学习过程。当前的互动增强学习研究仅限于实时互动，这些互动仅向当前状态提供相关的用户建议。此外，每次交互提供的信息均未保留，而是在一次使用后被代理商丢弃。在本文中，我们提出了一种保留和重复使用知识的方法，使培训师可以提供与当前状态相关的一般建议。结果表明，使用广泛的建议可以大大提高代理的性能，同时减少教练所需的相互作用数量。
The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained and instead discarded by the agent after a single use. In this paper, we present a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Results obtained show that the use of broad-persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer.
