  Inspired by recent work in attention models for image captioning and question
answering, we present a soft attention model for the reinforcement learning
domain. This model uses a soft, top-down attention mechanism to create a
bottleneck in the agent, forcing it to focus on task-relevant information by
sequentially querying its view of the environment. The output of the attention
mechanism allows direct observation of the information used by the agent to
select its actions, enabling easier interpretation of this model than of
traditional models. We analyze different strategies that the agents learn and
show that a handful of strategies arise repeatedly across different games. We
also show that the model learns to query separately about space and content
(`where' vs. `what'). We demonstrate that an agent using this mechanism can
achieve performance competitive with state-of-the-art models on ATARI tasks
while still being interpretable.
