Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

TL;DR

This paper introduces an attention-based reinforcement learning model that enhances interpretability by revealing the information used for decision-making, while maintaining competitive performance on ATARI games.

Contribution

It presents a novel soft attention mechanism for RL agents that improves interpretability without sacrificing performance.

Findings

Agents learn multiple strategies across games

The model queries space and content separately

Achieves state-of-the-art performance on ATARI

Abstract

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.