no code implementations • 1 Jan 2021 • Gregory Bonaert, Youri Coppens, Denis Steckelmacher, Ann Nowe
Our key contribution to improve explainability is introducing goal-based explanations, a new explanation mechanism where the agent produces goals and attempts to reach those goals one-by-one while maximizing the collected reward.
no code implementations • 26 Jan 2020 • Isel Grau, Dipankar Sengupta, Maria M. Garcia Lorenzo, Ann Nowe
In the context of some machine learning applications, obtaining data instances is a relatively easy process but labeling them could become quite expensive or tedious.
no code implementations • 10 Nov 2017 • Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon, Doina Precup, Ann Nowe
Generally, learning with longer options (like learning with multi-step returns) is known to be more efficient.
no code implementations • 11 Feb 2015 • Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowe
While PBRS is proven to always preserve optimal policies, its effect on learning speed is determined by the quality of its potential function, which, in turn, depends on both the underlying heuristic and the scale.
no code implementations • 21 May 2014 • Anna Harutyunyan, Tim Brys, Peter Vrancx, Ann Nowe
Recent advances of gradient temporal-difference methods allow to learn off-policy multiple value functions in parallel with- out sacrificing convergence guarantees or computational efficiency.