  First-person object-interaction tasks in high-fidelity, 3D, simulated
environments such as the AI2Thor virtual home-environment pose significant
sample-efficiency challenges for reinforcement learning (RL) agents learning
from sparse task rewards. To alleviate these challenges, prior work has
provided extensive supervision via a combination of reward-shaping,
ground-truth object-information, and expert demonstrations. In this work, we
show that one can learn object-interaction tasks from scratch without
supervision by learning an attentive object-model as an auxiliary task during
task learning with an object-centric relational RL agent. Our key insight is
that learning an object-model that incorporates object-attention into forward
prediction provides a dense learning signal for unsupervised representation
learning of both objects and their relationships. This, in turn, enables faster
policy learning for an object-centric relational RL agent. We demonstrate our
agent by introducing a set of challenging object-interaction tasks in the
AI2Thor environment where learning with our attentive object-model is key to
strong performance. Specifically, we compare our agent and relational RL agents
with alternative auxiliary tasks to a relational RL agent equipped with
ground-truth object-information, and show that learning with our object-model
best closes the performance gap in terms of both learning speed and maximum
success rate. Additionally, we find that incorporating object-attention into an
object-model's forward predictions is key to learning representations which
capture object-category and object-state.
