Entity-Centric Reinforcement Learning for Object Manipulation from
  Pixels
Dan Haramati Tal Daniel Aviv Tamar
Manipulating objects is a hallmark of human intelligence, and an important
task in domains such as robotics. In principle, Reinforcement Learning (RL)
offers a general approach to learn object manipulation. In practice, however,
domains with more than a few objects are difficult for RL agents due to the
curse of dimensionality, especially when learning from raw image observations.
In this work we propose a structured approach for visual RL that is suitable
for representing multiple objects and their interaction, and use it to learn
goal-conditioned manipulation of several objects. Key to our method is the
ability to handle goals with dependencies between the objects (e.g., moving
objects in a certain order). We further relate our architecture to the
generalization capability of the trained agent, based on a theoretical result
for compositional generalization, and demonstrate agents that learn with 3
objects but generalize to similar tasks with over 10 objects. Videos and code
are available on the project website:
https://sites.google.com/view/entity-centric-rl