Visualizing and Understanding Atari Agents

2017-10-31 · ICML 2018 7 · Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern

While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward explaining deep RL agents through a case study using Atari 2600 environments. In particular, we focus on using saliency maps to understand how an agent learns and executes a policy. We introduce a method for generating useful saliency maps and use it to show 1) what strong agents attend to, 2) whether agents are making decisions for the right or wrong reasons, and 3) how agents evolve during learning. We also test our method on non-expert human subjects and find that it improves their ability to reason about these agents. Overall, our results show that saliency information can provide significant insight into an RL agent's decisions and learning behavior.

📄 PDF Abstract BibTeX arXiv:1711.00138

Code (3)

greydanus/visualize_atari 공식 구현 pytorch

KDL-umass/saliency_maps

slowjazz/interactive-atari-RL pytorch

Tasks

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