188 citations · 880 across the 70 of their papers we have counts for
16 papers · 1 filter
Twice regularized MDPs and the equivalence between robustness and regularization
Esther Derman, Matthieu Geist, Shie Mannor
Robust Markov decision processes (MDPs) aim to handle changing or partially known system dynamics. To solve them, one typically resorts to robust optimization methods. However, thi…
Continuous Control with Action Quantization from Demonstrations
Robert Dadashi, Léonard Hussenot, Damien Vincent +4
In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The proposed…
Large Batch Experience Replay
Thibault Lahire, Matthieu Geist, Emmanuel Rachelson
Several algorithms have been proposed to sample non-uniformly the replay buffer of deep Reinforcement Learning (RL) agents to speed-up learning, but very few theoretical foundation…
Generalization in Mean Field Games by Learning Master Policies
Sarah Perrin, Mathieu Laurière, Julien Pérolat +3
Mean Field Games (MFGs) can potentially scale multi-agent systems to extremely large populations of agents. Yet, most of the literature assumes a single initial distribution for th…
Implicitly Regularized RL with Implicit Q-Values
Nino Vieillard, Marcin Andrychowicz, Anton Raichuk +2
The -function is a central quantity in many Reinforcement Learning (RL) algorithms for which RL agents behave following a (soft)-greedy policy w.r.t. to . It is a powerful to…
A general class of surrogate functions for stable and efficient reinforcement learning
Sharan Vaswani, Olivier Bachem, Simone Totaro +6
Common policy gradient methods rely on the maximization of a sequence of surrogate functions. In recent years, many such surrogate functions have been proposed, most without strong…