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As people learn to navigate the world, autonomic nervous system (e.g., "fightor flight") responses provide intrinsic feedback about the potentialconsequence of action choices (e.g., becoming nervous when close to a cliffedge or driving fast around a bend.) Physiological changes are correlated withthese biological preparations to protect one-self from danger. We present anovel approach to reinforcement learning that leverages a task-independentintrinsic reward function trained on peripheral pulse measurements that arecorrelated with human autonomic nervous system responses. Our hypothesis isthat such reward functions can circumvent the challenges associated with sparseand skewed rewards in reinforcement learning settings and can help improvesample efficiency. We test this in a simulated driving environment and showthat it can increase the speed of learning and reduce the number of collisionsduring the learning stage.

Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards