Visceral Machines: Reinforcement Learning with Intrinsic Rewards that Mimic the Human Nervous System
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The human autonomic nervous system has evolved over millions of years and is essential for survival and responding to threats. As people learn to navigate the world, "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.) We present a novel approach to reinforcement learning that leverages a task-independent intrinsic reward function that mimics human autonomic nervous system responses based on peripheral pulse measurements. 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.
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