67 citations · 110 across the 10 of their papers we have counts for
12 papers
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Lucas N. Alegre, Ana L. C. Bazzan, Diederik M. Roijers +2
Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward function…
Model-Based Reinforcement Learning with SINDy
Rushiv Arora, Bruno Castro da Silva, Eliot Moss
We draw on the latest advancements in the physics community to propose a novel method for discovering the governing non-linear dynamics of physical systems in reinforcement learnin…
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
Lucas N. Alegre, Ana L. C. Bazzan, Bruno C. da Silva
In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are ex…
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…
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control
Lucas N. Alegre, Ana L. C. Bazzan, Bruno C. da Silva
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for a…
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…
A Compression-Inspired Framework for Macro Discovery
Francisco M. Garcia, Bruno C. da Silva, Philip S. Thomas
In this paper we consider the problem of how a reinforcement learning agent tasked with solving a set of related Markov decision processes can use knowledge acquired early in its l…
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…
Learning Parameterized Skills
Bruno Da Silva, George Konidaris, Andrew Barto
We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks fr…