66 citations · 104 across the 8 of their papers we have counts for
4 papers · 1 filter
Exploration Conscious Reinforcement Learning Revisited
Lior Shani, Yonathan Efroni, Shie Mannor
The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of…
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…