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pro vyhledávání: '"Wiedmann, Marco"'
Autor:
Wiedmann, Marco, Hölle, Marc, Periyasamy, Maniraman, Meyer, Nico, Ufrecht, Christian, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
VQA have attracted a lot of attention from the quantum computing community for the last few years. Their hybrid quantum-classical nature with relatively shallow quantum circuits makes them a promising platform for demonstrating the capabilities of NI
Externí odkaz:
http://arxiv.org/abs/2305.00224
Autor:
Periyasamy, Maniraman, Hölle, Marc, Wiedmann, Marco, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
Deep reinforcement learning (DRL) often requires a large number of data and environment interactions, making the training process time-consuming. This challenge is further exacerbated in the case of batch RL, where the agent is trained solely on a pr
Externí odkaz:
http://arxiv.org/abs/2305.00905
Autor:
Periyasamy, Maniraman, Hölle, Marc, Wiedmann, Marco, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
Training DRL agents is often a time-consuming process as a large number of samples and environment interactions is required. This effect is even amplified in the case of Batch RL, where the agent is trained without environment interactions solely bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07e681cf5dcee8aa08691b60010c2150
Akademický článek
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