4 research outputs found
Neuroevolution of Self-Interpretable Agents
Inattentional blindness is the psychological phenomenon that causes one to
miss things in plain sight. It is a consequence of the selective attention in
perception that lets us remain focused on important parts of our world without
distraction from irrelevant details. Motivated by selective attention, we study
the properties of artificial agents that perceive the world through the lens of
a self-attention bottleneck. By constraining access to only a small fraction of
the visual input, we show that their policies are directly interpretable in
pixel space. We find neuroevolution ideal for training self-attention
architectures for vision-based reinforcement learning (RL) tasks, allowing us
to incorporate modules that can include discrete, non-differentiable operations
which are useful for our agent. We argue that self-attention has similar
properties as indirect encoding, in the sense that large implicit weight
matrices are generated from a small number of key-query parameters, thus
enabling our agent to solve challenging vision based tasks with at least 1000x
fewer parameters than existing methods. Since our agent attends to only task
critical visual hints, they are able to generalize to environments where task
irrelevant elements are modified while conventional methods fail. Videos of our
results and source code available at https://attentionagent.github.io/Comment: To appear at the Genetic and Evolutionary Computation Conference
(GECCO 2020) as a full pape