1 citations · 3 across the 8 of their papers we have counts for
8 papers
Alignment among Language, Vision and Action Representations
Nicola Milano, Stefano Nolfi
A fundamental question in cognitive science and AI concerns whether different learning modalities: language, vision, and action, give rise to distinct or shared internal representa…
Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents
Nicola Milano, Stefano Nolfi
In this paper we analyze the qualitative differences between evolutionary strategies and reinforcement learning algorithms by focusing on two popular state-of-the-art algorithms: t…
Automated Curriculum Learning for Embodied Agents: A Neuroevolutionary Approach
Nicola Milano, Stefano Nolfi
We demonstrate how an evolutionary algorithm can be extended with a curriculum learning process that selects automatically the environmental conditions in which the evolving agents…
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents
Nicola Milano, Stefano Nolfi
As discussed in previous studies, the efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including a neural module…
Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
Paolo Pagliuca, Nicola Milano, Stefano Nolfi
We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall, the results collected on a wide variety of qualitatively different ben…
Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions
Nicola Milano, Stefano Nolfi
We demonstrate how efficiency of Cartesian Genetic Programming method can be scaled up through the preferential selection of phenotypically larger solutions, i.e. through the prefe…
Robustness, Evolvability and Phenotypic Complexity: Insights from Evolving Digital Circuits
Nicola Milano, Paolo Pagliuca, Stefano Nolfi
We show how the characteristics of the evolutionary algorithm influence the evolvability of candidate solutions, i.e. the propensity of evolving individuals to generate better solu…
Moderate Environmental Variation Promotes the Evolution of Robust Solutions
Nicola Milano, Jônata Tyska Carvalho, Stefano Nolfi
Previous evolutionary studies demonstrated how evaluating evolving agents in variable environmental conditions enable them to develop solutions that are robust to environmental var…