Combinatorial optimization problems (COPs) are a class of NP-hard problems with great
practical significance. Traditional approaches for COPs suffer from high computational time …

Investigation of value based Reinforcement Learning algorithms exhibited a resurgence into
mainstream research in 2015 following demonstration of super-human performance when …

Learning how to adapt to complex and dynamic environments is one of the most important
factors that contribute to our intelligence. Endowing artificial agents with this ability is not a …

Designing the decision-making processes of artificial agents that are involved in competitive
interactions is a challenging task. In a competitive scenario, the agent does not only have a …

Abstract Modern deep Reinforcement Learning (RL) methods are highly effective at
selecting optimal policies to maximize rewards. The combination of these methods with …

This work evaluates competition in training of autonomous agents immersed in First-Person
Shooter games using Deep Reinforcement Learning. The agents are composed of a Deep …

The task of Person re-identification (re-ID) is to recognize an individual observed by non-
overlapping cameras. Robust feature representation is a crucial problem in re-ID. With the …

Making a choice is a complex task, involving many other processes that can heavily
influence the final decision: experience plays a fundamental role in order to determine the …

Sparse codes in neuroscience have been suggested to offer certain computational
advantages over other neural representations of sensory data. To explore this viewpoint, a …

Ivanov I. Analysis of the phaunistic composition of Ukraine//Scientific progress: innovations,
achievements and prospects. Proceedings of the 5th International scientific and practical …