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I-Shaped Developer
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Dataset of human fNIRS signals during interaction with a Reinforcement Learning agent.
This repository tests whether agent can learn ethical norms through learning by demonstration. We test a range of quality across demonstration sets.
This project decodes fNIRS signals from participants interacting with an RL agent and explores methods to classify these signals based on agent performance/optimality. Mapping fNIRS signals to various levels of agent performance will allow us to implicitly update agent learning algorithms through neural feedback.
Code for training, evaluating, and visualizing performance of neural reward classification using fNIRS BCI sensors
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