March, 2021 - ML cube

13 Mar 21

Machine Learning Models Life Cycle

In the last years, always more companies started introducing AI systems impacting their business. Machine Learning is used in e-commerce, advertisement, health, finances, driving, industry, and many other contexts. Besides the big tech players such as Google, Microsoft, Amazon and IBM, many new startups born each year raising investments over $40.4B in 2018 (see page […]

05 Mar 21

Policy Optimization as Online Learning with Mediator Feedback

Policy Optimization as Online Learning with Mediator Feedback Authors: Alberto Maria Metelli, Matteo Papini, Pierluca D’Oro, Marcello Restelli Conference: AAAI 2021 Abstract: 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 learning problem over the […]

05 Mar 21

Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate

Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate Authors: Mirco Mutti, Lorenzo Pratissoli, Marcello Restelli Conference: AAAI 2021 Abstract: In a reward-free environment, what is a suitable intrinsic objective for an agent to pursue so that it can learn an optimal task-agnostic exploration policy? In this paper, we argue that the entropy […]

05 Mar 21

Newton Optimization on Helmholtz Decomposition for Continuous Games

Newton Optimization on Helmholtz Decomposition for Continuous Games Authors: Giorgia Ramponi, Marcello Restelli Conference: AAAI 2021 Abstract: Many learning problems involve multiple agents optimizing different interactive functions. In these problems, the standard policy gradient algorithms fail due to the non-stationarity of the setting and the different interests of each agent. In fact, algorithms must take […]