90 citations · 194 across the 7 of their papers we have counts for
4 papers · 1 filter
Anderson Acceleration for Reinforcement Learning
Matthieu Geist, Bruno Scherrer
Anderson acceleration is an old and simple method for accelerating the computation of a fixed point. However, as far as we know and quite surprisingly, it has never been applied to…
How to Combine Tree-Search Methods in Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer +1
Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success. Usually, the lookahead policies are implemented with s…
Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer +1
Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a rec…
Beyond the One Step Greedy Approach in Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer +1
The famous Policy Iteration algorithm alternates between policy improvement and policy evaluation. Implementations of this algorithm with several variants of the latter evaluation…