Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Understand

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.