Google Introduces Neuroevolution for Self-Interpretable Agents

Good gamers can tune out distractions and unimportant on-screen information and focus their attention on avoiding obstacles and overtaking others in virtual racing games like Mario Kart. However, can machines behave similarly in such vision-based tasks? A possible solution is designing agents that encode and process abstract concepts, and research in this area has focused on learning all abstract information from visual inputs. This however is compute intensive and can even degrade model performance. Now, researchers from Google Brain Tokyo and Google Japan have proposed a novel approach that helps guide reinforcement learning (RL) agents to what's important in vision-based tasks.