3 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Autonomous Reinforcement Learning of Multiple Interrelated Tasks
Vieri Giuliano Santucci, Gianluca Baldassarre, Emilio Cartoni
Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be in…