188 citations · 880 across the 70 of their papers we have counts for
8 papers · 2 filters
On Connections between Constrained Optimization and Reinforcement Learning
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Dynamic Programming (DP) provides standard algorithms to solve Markov Decision Processes. However, these algorithms generally do not optimize a scalar objective function. In this p…
Momentum in Reinforcement Learning
Nino Vieillard, Bruno Scherrer, Olivier Pietquin +1
We adapt the optimization's concept of momentum to reinforcement learning. Seeing the state-action value functions as an analog to the gradients in optimization, we interpret momen…
Self-Attentional Credit Assignment for Transfer in Reinforcement Learning
Johan Ferret, Raphaël Marinier, Matthieu Geist +1
The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents. Despite the apparent promises, transfer in RL is still an op…
Modified Actor-Critics
Erinc Merdivan, Sten Hanke, Matthieu Geist
Recent successful deep reinforcement learning algorithms, such as Trust Region Policy Optimization (TRPO) or Proximal Policy Optimization (PPO), are fundamentally variations of con…
MULEX: Disentangling Exploitation from Exploration in Deep RL
Lucas Beyer, Damien Vincent, Olivier Teboul +3
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the…
Deep Conservative Policy Iteration
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP). Its core principle is to stabilize greediness through stochastic mixtures of c…