3 citations · 4 across the 7 of their papers we have counts for
10 papers
Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies
Alejandro Romero, Gianluca Baldassarre, Richard J. Duro +1
Autonomous open-ended learning is a relevant approach in machine learning and robotics, allowing the design of artificial agents able to acquire goals and motor skills without the…
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning
Giovanni Granato, Emilio Cartoni, Federico Da Rold +2
A common view on the brain learning processes proposes that the three classic learning paradigms -- unsupervised, reinforcement, and supervised -- take place in respectively the co…
Learning Abstract Representations through Lossy Compression of Multi-Modal Signals
Charles Wilmot, Gianluca Baldassarre, Jochen Triesch
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific det…
Autonomous learning of multiple, context-dependent tasks
Vieri Giuliano Santucci, Davide Montella, Bruno Castro da Silva +1
When facing the problem of autonomously learning multiple tasks with reinforcement learning systems, researchers typically focus on solutions where just one parametrised policy per…
Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints
Manuel Del Verme, Bruno Castro da Silva, Gianluca Baldassarre
Reinforcement learning can greatly benefit from the use of options as a way of encoding recurring behaviours and to foster exploration. An important open problem is how can an agen…
Intrinsic motivations and open-ended learning
Gianluca Baldassarre
There is a growing interest and literature on intrinsic motivations and open-ended learning in both cognitive robotics and machine learning on one side, and in psychology and neuro…