1 citations · 1 across the 5 of their papers we have counts for
5 papers
Locally-Guided Actor-Critic: Training a Goal-conditioned Actor with a Subgoal-aware Critic
Olivier Serris, Stéphane Doncieux, Olivier Sigaud
Goal-conditioned reinforcement learning struggles with long horizons when rewards are sparse. While a planner can provide subgoals to guide a low-level policy, its use at test time…
A tale of two goals: leveraging sequentiality in multi-goal scenarios
Olivier Serris, Stéphane Doncieux, Olivier Sigaud
Several hierarchical reinforcement learning methods leverage planning to create a graph or sequences of intermediate goals, guiding a lower-level goal-conditioned (GC) policy to re…
Single-Reset Divide & Conquer Imitation Learning
Alexandre Chenu, Olivier Serris, Olivier Sigaud +1
Demonstrations are commonly used to speed up the learning process of Deep Reinforcement Learning algorithms. To cope with the difficulty of accessing multiple demonstrations, some…
Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration
Alexandre Chenu, Olivier Serris, Olivier Sigaud +1
Deep Reinforcement Learning has been successfully applied to learn robotic control. However, the corresponding algorithms struggle when applied to problems where the agent is only…
Help Me Explore: Minimal Social Interventions for Graph-Based Autotelic Agents
Ahmed Akakzia, Olivier Serris, Olivier Sigaud +1
In the quest for autonomous agents learning open-ended repertoires of skills, most works take a Piagetian perspective: learning trajectories are the results of interactions between…