Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning

In many reinforcement learning tasks, the agent has to learn to interact with many objects of different types and generalize to unseen combinations and numbers of objects. Often a task is a composition of previously learned tasks (e.g.