1 citations · 1 across the 2 of their papers we have counts for
3 papers
Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes
Luca Sabbioni, Francesco Corda, Marcello Restelli
Policy-based algorithms are among the most widely adopted techniques in model-free RL, thanks to their strong theoretical groundings and good properties in continuous action spaces…
Simultaneously Updating All Persistence Values in Reinforcement Learning
Luca Sabbioni, Luca Al Daire, Lorenzo Bisi +2
In reinforcement learning, the performance of learning agents is highly sensitive to the choice of time discretization. Agents acting at high frequencies have the best control oppo…
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi +2
The choice of the control frequency of a system has a relevant impact on the ability of reinforcement learning algorithms to learn a highly performing policy. In this paper, we int…