论文标题
基于模型的强化学习中的Delta模式网络
Delta Schema Network in Model-based Reinforcement Learning
论文作者
论文摘要
这项工作致力于未解决的人工通用智能问题 - 转移学习效率低下。在增强学习领域中用于解决此问题的机制之一是一种基于模型的方法。在本文中，我们正在扩展架构网络方法，该方法允许从环境数据中提取对象和操作之间的逻辑关系。我们介绍了培训三角示例网络（DSN）的算法，预测环境的未来状态和计划行动，这将带来积极的回报。 DSN在经典的Atari游戏环境中表现出强大的转移学习表现。
This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning. One of the mechanisms that are used to solve this problem in the area of reinforcement learning is a model-based approach. In the paper we are expanding the schema networks method which allows to extract the logical relationships between objects and actions from the environment data. We present algorithms for training a Delta Schema Network (DSN), predicting future states of the environment and planning actions that will lead to positive reward. DSN shows strong performance of transfer learning on the classic Atari game environment.
