Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
This work addresses the need for better interpretability and safety in AI systems, particularly for visual-based reinforcement learning, though it is incremental in building on existing generative models.
The authors tackled the problem of understanding deep reinforcement learning agents by developing a method to synthesize visual inputs that reveal weaknesses, such as critical states with extreme rewards, and demonstrated its effectiveness across Atari games and an autonomous driving simulator.
As deep reinforcement learning driven by visual perception becomes more widely used there is a growing need to better understand and probe the learned agents. Understanding the decision making process and its relationship to visual inputs can be very valuable to identify problems in learned behavior. However, this topic has been relatively under-explored in the research community. In this work we present a method for synthesizing visual inputs of interest for a trained agent. Such inputs or states could be situations in which specific actions are necessary. Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states. To this end, we learn a generative model over the state space of the environment and use its latent space to optimize a target function for the state of interest. In our experiments we show that this method can generate insights for a variety of environments and reinforcement learning methods. We explore results in the standard Atari benchmark games as well as in an autonomous driving simulator. Based on the efficiency with which we have been able to identify behavioural weaknesses with this technique, we believe this general approach could serve as an important tool for AI safety applications.