5 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…
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…
Learning High-Level Planning Symbols from Intrinsically Motivated Experience
Angelo Oddi, Riccardo Rasconi, Emilio Cartoni +3
In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to cre…
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…
Autonomous Open-Ended Learning of Interdependent Tasks
Vieri Giuliano Santucci, Emilio Cartoni, Bruno Castro da Silva +1
Autonomy is fundamental for artificial agents acting in complex real-world scenarios. The acquisition of many different skills is pivotal to foster versatile autonomous behaviour a…