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Inspired by recent work in attention models for image captioning and questionanswering, we present a soft attention model for the reinforcement learningdomain. This model uses a soft, top-down attention mechanism to create abottleneck in the agent, forcing it to focus on task-relevant information bysequentially querying its view of the environment. The output of the attentionmechanism allows direct observation of the information used by the agent toselect its actions, enabling easier interpretation of this model than oftraditional models. We analyze different strategies that the agents learn andshow that a handful of strategies arise repeatedly across different games. Wealso show that the model learns to query separately about space and content(`where' vs. `what'). We demonstrate that an agent using this mechanism canachieve performance competitive with state-of-the-art models on ATARI taskswhile still being interpretable.

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents