Description
Session chair: Daniel Mastropietro
Deep Reinforcement Learning (DRL) has showcased remarkable achievements across various domains, such as image recognition and automation. Nevertheless, its potential in the realm of logistics and transportation, particularly in tackling routing challenges, remains mostly untapped. On the contrary, Evolutionary Algorithms (EA) have enjoyed widespread adoption for solving combinatorial...
Downtime of industrial assets such as wind turbines and medical imaging devices is costly. To avoid such downtime costs, companies seek to initiate maintenance just before failure, which is challenging because: (i) Asset failures are notoriously difficult to predict, even in the presence of real-time monitoring devices that signal degradation; and (ii) Limited resources are available to serve...
In this talk we present Fleming-Viot particle systems to increase the efficiency in discovering rare events that have an impact in the learning speed of optimal policies. The approach is used to learn the critic of Actor-Critic policy gradient methods that learn optimal parameters of parameterized policies, giving rise to what we call the FVAC method. We have successfully applied FVAC to two...
