10 citations · 18 across the 5 of their papers we have counts for
6 papers
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection
Lucas N. Alegre, Ana L. C. Bazzan, Bruno C. da Silva
Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effe…
Universal Off-Policy Evaluation
Yash Chandak, Scott Niekum, Bruno Castro da Silva +3
When faced with sequential decision-making problems, it is often useful to be able to predict what would happen if decisions were made using a new policy. Those predictions must of…
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…
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…
On Ensuring that Intelligent Machines Are Well-Behaved
Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto +1
Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human per…