A Triche, AS Maida, A Kumar - Neural Networks, 2022 - Elsevier
Recent theoretical and experimental works have connected Hebbian plasticity with the reinforcement learning (RL) paradigm, producing a class of trial-and-error learning in …
Novelty and surprise play significant roles in animal behavior and in attempts to understand the neural mechanisms underlying it. They also play important roles in technology, where …
In this paper, we present goal-discovering robotic architecture for intrisically-motivated learning (GRAIL), a four-level architecture that is able to autonomously: 1) discover changes …
T Li, S Duan, J Liu, L Wang… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
A radial basis function (RBF) neural network control algorithm can effectively improve the robotic manipulators' performance against a large amount of uncertainty. The adaptive law …
This editorial article introduces the Frontiers Research Topic and Electronic Book (eBook) on Intrinsic Motivations (IMs), which involved the publication of 24 articles with the journals …
Humans and other biological agents are able to autonomously learn and cache different skills in the absence of any biological pressure or any assigned task. In this respect, Intrinsic …
M Mirolli, G Baldassarre - Intrinsically motivated learning in natural and …, 2013 - Springer
Mammals, and humans in particular, are endowed with an exceptional capacity for cumulative learning. This capacity crucially depends on the presence of intrinsic motivations …
Abstract Reinforcement (trial-and-error) learning in animals is driven by a multitude of processes. Most animals have evolved several sophisticated systems of 'extrinsic …
Designing robots has usually implied knowing beforehand the tasks to be carried out and in what domains. However, in the case of fully autonomous robots this is not possible …