 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.
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.  Proposes a reinforcement learning framework based on human emotional reaction in the context of autonomous driving. The authors propose to use signals, such as basic autonomic visceral responses that influence decision-making, within the RL framework by augmenting RL reward functions with a model learned directly from human nervous system responses. Proposes to use physiological signals to improve performance of reinforcement learning algorithms and build an intrinsic reward function that is less sparse by measuring heart pulse amplitude