7 papers
Policy Optimization as Online Learning with Mediator Feedback
Alberto Maria Metelli, Matteo Papini, Pierluca D'Oro +1
Policy Optimization (PO) is a widely used approach to address continuous control tasks. In this paper, we introduce the notion of mediator feedback that frames PO as an online lear…
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…
Policy Space Identification in Configurable Environments
Alberto Maria Metelli, Guglielmo Manneschi, Marcello Restelli
We study the problem of identifying the policy space of a learning agent, having access to a set of demonstrations generated by its optimal policy. We introduce an approach based o…
Gradient-Aware Model-based Policy Search
Pierluca D'Oro, Alberto Maria Metelli, Andrea Tirinzoni +2
Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence…
Feature Selection via Mutual Information: New Theoretical Insights
Mario Beraha, Alberto Maria Metelli, Matteo Papini +2
Mutual information has been successfully adopted in filter feature-selection methods to assess both the relevancy of a subset of features in predicting the target variable and the…
Policy Optimization via Importance Sampling
Alberto Maria Metelli, Matteo Papini, Francesco Faccio +1
Policy optimization is an effective reinforcement learning approach to solve continuous control tasks. Recent achievements have shown that alternating online and offline optimizati…