  Positive affect has been linked to increased interest, curiosity and
satisfaction in human learning. In reinforcement learning, extrinsic rewards
are often sparse and difficult to define, intrinsically motivated learning can
help address these challenges. We argue that positive affect is an important
intrinsic reward that effectively helps drive exploration that is useful in
gathering experiences. We present a novel approach leveraging a
task-independent reward function trained on spontaneous smile behavior that
reflects the intrinsic reward of positive affect. To evaluate our approach we
trained several downstream computer vision tasks on data collected with our
policy and several baseline methods. We show that the policy based on our
affective rewards successfully increases the duration of episodes, the area
explored and reduces collisions. The impact is the increased speed of learning
for several downstream computer vision tasks.
