205 citations · 777 across the 38 of their papers we have counts for
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cs.LG2021★ 2 cited
Unbiased Methods for Multi-Goal Reinforcement Learning
Léonard Blier, Yann Ollivier
In multi-goal reinforcement learning (RL) settings, the reward for each goal is sparse, and located in a small neighborhood of the goal. In large dimension, the probability of reac…
cs.LG2021★ 5 cited
Learning One Representation to Optimize All Rewards
Ahmed Touati, Yann Ollivier
We introduce the forward-backward (FB) representation of the dynamics of a reward-free Markov decision process. It provides explicit near-optimal policies for any reward specified…
cs.LG2021
Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint
Léonard Blier, Corentin Tallec, Yann Ollivier
In reinforcement learning, temporal difference-based algorithms can be sample-inefficient: for instance, with sparse rewards, no learning occurs until a reward is observed. This ca…