Visceral Machines: Risk-Aversion in Reinforcement Learning with
  Intrinsic Physiological Rewards
  As people learn to navigate the world, autonomic nervous system (e.g., "fight
or flight") responses provide intrinsic feedback about the potential
consequence of action choices (e.g., becoming nervous when close to a cliff
edge or driving fast around a bend.) Physiological changes are correlated with
these biological preparations to protect one-self from danger. We present a
novel approach to reinforcement learning that leverages a task-independent
intrinsic reward function trained on peripheral pulse measurements that are
correlated with human autonomic nervous system responses. Our hypothesis is
that such reward functions can circumvent the challenges associated with sparse
and skewed rewards in reinforcement learning settings and can help improve
sample efficiency. We test this in a simulated driving environment and show
that it can increase the speed of learning and reduce the number of collisions
during the learning stage.
