REINFORCEMENT LEARNING SYSTEMS COMPRISING A RELATIONAL NETWORK FOR GENERATING DATA ENCODING RELATIONSHIPS BETWEEN ENTITIES IN AN ENVIRONMENT

A neural network system for reinforcement learning is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network isarranged to receive data output by the relational network, and use the received data to select a respective action (e.g. an action from a predefined space of possible actions. 提出了一种用于强化学习的神经网络系统，包括用于从状态数据中为环境中存在或至少潜在存在的多个实体中的