4 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
External control of a genetic toggle switch via Reinforcement Learning
Sara Maria Brancato, Francesco De Lellis, Davide Salzano +2
We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach. To overcome the data efficiency problem that…
Control-Tutored Reinforcement Learning
Francesco De Lellis, Fabrizia Auletta, Giovanni Russo +2
We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms so as to improve the exploration of the state-space, and…
Control-Tutored Reinforcement Learning: an application to the Herding Problem
Francesco De Lellis, Fabrizia Auletta, Giovanni Russo +1
In this extended abstract we introduce a novel control-tutored Q-learning approach (CTQL) as part of the ongoing effort in developing model-based and safe RL for continuous state s…