  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. block stacking). These are examples of compositional generalization, in
which we compose object-centric representations to solve complex tasks. Recent
works have shown the benefits of object-factored representations and
hierarchical abstractions for improving sample efficiency in these settings. On
the other hand, these methods do not fully exploit the benefits of
factorization in terms of object attributes. In this paper, we address this
opportunity and introduce the Dynamic Attribute FacTored RL (DAFT-RL)
framework. In DAFT-RL, we leverage object-centric representation learning to
extract objects from visual inputs. We learn to classify them in classes and
infer their latent parameters. For each class of object, we learn a class
template graph that describes how the dynamics and reward of an object of this
class factorize according to its attributes. We also learn an interaction
pattern graph that describes how objects of different classes interact with
each other at the attribute level. Through these graphs and a dynamic
interaction graph that models the interactions between objects, we can learn a
policy that can then be directly applied in a new environment by just
estimating the interactions and latent parameters. We evaluate DAFT-RL in three
benchmark datasets and show our framework outperforms the state-of-the-art in
generalizing across unseen objects with varying attributes and latent
parameters, as well as in the composition of previously learned tasks.
