A pilot study on the evolution of reward signals for hierarchical reinforcement learning

Recent research has shown that reinforcement learning agents can by greatly advantaged of the possibility of learning to select macro actions instead, or beside, fine primitive actions. The route usually followed to exploit this idea is to build agents with hierarchical architectures that can learn both a repertoire of macro actions and a macro policy that selects them, on the basis of the "final" reward signals related to the tasks to solve. This research presents a pre-liminary investigation that follows a different idea: evolving reinforcement signals that allow an agent to learn a repertoire of macro actions in an initial phase of life, and then to assemble and use them to solve different tasks, belonging to the same class, in a succeeding phase of life. The idea is preliminary tested with a simulated robot that has to search targets following coloured trails in a square arena. Results show that the idea is viable and that the emerged reinforcement signals allow the robot to develop macro actions that are actually useful to solve different tasks in succeeding phase of life

An enactive approach to size constancy

The purpose of my work is to explore the dynamical aspects of size constancy with a research approach that gives more emphasis on embodiment and situatedness. Drawing inspiration from the enactive approach to cognition (Valera, Thompson, & Rosch, 1991) , I study the role of active motion in size constancy with an interdisciplinary approach that combines two different methodologies: artificial life modeling and adaptive psychophysical methods

Apprendimento cumulativo basato su motivazioni intrinseche: un modello neurale gerarchico testato su un robot simulato

No abstract availabl

Training disaster communication by means of serious games in virtual environments

The training of social skills in organizational settings has become more and more important for an effective communicative exchange between members of staff. Especially in companies where the line of communication has to be fast and unmistakable, e.g. in crisis management units, the regular training of communication skills is therefore indispensable. The DREAD-ED project proposes an innovative, technology-based teaching methodology to meet these needs. The methodology provides a serious game which enables its users to train soft skills in a virtual environment under safe conditions. The current paper presents the results of two trials conducted with crisis managers and university students in Germany

Costanza percettiva: ben oltre l’estrazione di caratteristiche

L’obbiettivo di questo contributo è dare un’idea della complessità e dell'importanza del fenomeno della costanza percettiva attraverso una mappatura dei vari aspetti del problema quale emerge dalla vasta letteratura psicologica disponibile in materia. Emergono in questo modo i limiti degli algoritmi di estrazione delle caratteristiche e del concetto di invarianza nel catturare la complessità di questo importante aspetto della cognizione

Evolving reinforcement signals to learn macro actions useful for different navigation tasks

Recent research has shown that reinforcement learning agents can by greatly advantaged of the possibility of learning to select macro actions instead, or beside, fine primitive actions. The route usually followed to exploit this idea is to build agents with hierarchical architectures that can learn both a repertoire of macro actions and a macro policy that selects them, on the basis of the "final" reward signals related to the tasks to solve. This technical report presents the experimental setup that will be used to carry out a preliminary investigation that follows a different idea: evolving reinforcement signals that allow an agent to learn a repertoire of macro actions in an initial phase, and then to assemble and use them to solve different tasks, belonging to the same class, in a succeeding phase. The preliminary tests carried out with the setup presented here (not presented here), run with a simulated robot that has to search targets in an arena with a ground having various textures, show that the idea is viable and that the emerged reinforcement signals allow the robot to develop macro actions that are actually useful to solve different tasks