22 citations · 66 across the 21 of their papers we have counts for
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cs.LG2022★ 4 cited
CT-DQN: Control-Tutored Deep Reinforcement Learning
Francesco De Lellis, Marco Coraggio, Giovanni Russo +2
One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Contr…
cs.LG2021★ 3 cited
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control
F. De Lellis, M. Coraggio, G. Russo +2
We present an architecture where a feedback controller derived on an approximate model of the environment assists the learning process to enhance its data efficiency. This architec…