  Deep reinforcement learning (DRL) has shown incredible performance in
learning various tasks to the human level. However, unlike human perception,
current DRL models connect the entire low-level sensory input to the
state-action values rather than exploiting the relationship between and among
entities that constitute the sensory input. Because of this difference, DRL
needs vast amount of experience samples to learn. In this paper, we propose a
Multi-focus Attention Network (MANet) which mimics human ability to spatially
abstract the low-level sensory input into multiple entities and attend to them
simultaneously. The proposed method first divides the low-level input into
several segments which we refer to as partial states. After this segmentation,
parallel attention layers attend to the partial states relevant to solving the
task. Our model estimates state-action values using these attended partial
states. In our experiments, MANet attains highest scores with significantly
less experience samples. Additionally, the model shows higher performance
compared to the Deep Q-network and the single attention model as benchmarks.
Furthermore, we extend our model to attentive communication model for
performing multi-agent cooperative tasks. In multi-agent cooperative task
experiments, our model shows 20% faster learning than existing state-of-the-art
model.
