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The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 5829–5833 (IEEE, 2018). Sequence-to-aequence ASR optimization via reinforcement learning. In 2019 International Conference on Robotics and Automation (ICRA) 6023–6029 (IEEE, 2019).
Residual reinforcement learning for robot control. Speeding-up the decision making of a learning agent using an ion trap quantum processor. Quantum speedup for active learning agents. D., Dunjiko, V., Makmal, A., Martin-Delgrado, M. Reinforcement Learning: An Introduction (MIT Press, 1998).ĭunjko, V., Taylor, J. The device interfaces with telecommunication-wavelength photons and features a fast active-feedback mechanism, demonstrating the agent’s systematic quantum advantage in a setup that could readily be integrated within future large-scale quantum communication networks. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. We further show that combining this scenario with classical communication enables the evaluation of this improvement and allows optimal control of the learning progress. Here we present a reinforcement learning experiment in which the learning process of an agent is sped up by using a quantum communication channel with the environment. Although various studies have made use of quantum mechanics to speed up the agent’s decision-making process 3, 4, a reduction in learning time has not yet been demonstrated. The crucial question for practical applications is how fast agents learn 2. An important paradigm within artificial intelligence is reinforcement learning 1, where decision-making entities called agents interact with environments and learn by updating their behaviour on the basis of the obtained feedback. As the field of artificial intelligence advances, the demand for algorithms that can learn quickly and efficiently increases.
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