62 citations · 64 across the 5 of their papers we have counts for
5 papers
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…
Augmented Bayesian Policy Search
Mahdi Kallel, Debabrota Basu, Riad Akrour +1
Deterministic policies are often preferred over stochastic ones when implemented on physical systems. They can prevent erratic and harmful behaviors while being easier to implement…
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
Carlo D'Eramo, Davide Tateo, Andrea Bonarini +2
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption tha…
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
Aryaman Reddi, Maximilian Tölle, Jan Peters +2
Robustness against adversarial attacks and distribution shifts is a long-standing goal of Reinforcement Learning (RL). To this end, Robust Adversarial Reinforcement Learning (RARL)…
Monte-Carlo tree search with uncertainty propagation via optimal transport
Tuan Dam, Pascal Stenger, Lukas Schneider +3
This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) designed for highly stochastic and partially observable Markov decision processes. We adopt a proba…