Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning

TL;DR

This paper introduces Mask-Attention A3C, an attention mechanism integrated into deep reinforcement learning that visualizes decision-making and improves performance in game environments.

Contribution

It proposes a novel attention mechanism for actor-critic DRL that enables visualization of decision reasons and enhances agent performance.

Findings

Attention maps effectively visualize decision reasons.

The method improves agent performance in Atari games.

Attention mechanism maintains or enhances learning efficiency.

Abstract

Deep reinforcement learning (DRL) has great potential for acquiring the optimal action in complex environments such as games and robot control. However, it is difficult to analyze the decision-making of the agent, i.e., the reasons it selects the action acquired by learning. In this work, we propose Mask-Attention A3C (Mask A3C), which introduces an attention mechanism into Asynchronous Advantage Actor-Critic (A3C), which is an actor-critic-based DRL method, and can analyze the decision-making of an agent in DRL. A3C consists of a feature extractor that extracts features from an image, a policy branch that outputs the policy, and a value branch that outputs the state value. In this method, we focus on the policy and value branches and introduce an attention mechanism into them. The attention mechanism applies a mask processing to the feature maps of each branch using mask-attention that expresses the judgment reason for the policy and state value with a heat map. We visualized mask-attention maps for games on the Atari 2600 and found we could easily analyze the reasons behind an agent's decision-making in various game tasks. Furthermore, experimental results showed that the agent could achieve a higher performance by introducing the attention mechanism.