1 citations · 2 across the 2 of their papers we have counts for
3 papers
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…
Delayed Reinforcement Learning by Imitation
Pierre Liotet, Davide Maran, Lorenzo Bisi +1
When the agent's observations or interactions are delayed, classic reinforcement learning tools usually fail. In this paper, we propose a simple yet new and efficient solution to t…
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…