188 citations · 880 across the 70 of their papers we have counts for
4 papers · 1 filter
On the importance of data collection for training general goal-reaching policies
Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold +3
Recent advances in ML suggest that the quantity of data available to a model is one of the primary bottlenecks to high performance. Although for language-based tasks there exist al…
Scaling up Mean Field Games with Online Mirror Descent
Julien Perolat, Sarah Perrin, Romuald Elie +5
We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash equilibriu…
Off-policy Learning with Eligibility Traces: A Survey
Matthieu Geist, Bruno Scherrer
In the framework of Markov Decision Processes, off-policy learning, that is the problem of learning a linear approximation of the value function of some fixed policy from one traje…
Approximate Modified Policy Iteration
Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh +1
Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that contains the two celebrated policy and value iteration methods. Despite its generality, MPI has not bee…