275 citations · 437 across the 32 of their papers we have counts for
5 papers · 1 filter
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…
Safe Exploration in Continuous Action Spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik +3
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be vi…
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Gal Dalal, Elad Gilboa, Shie Mannor +1
Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its objective is to m…