278 citations · 588 across the 21 of their papers we have counts for
18 papers · 1 filter
Offline Learning of Controllable Diverse Behaviors
Mathieu Petitbois, Rémy Portelas, Sylvain Lamprier +1
Imitation Learning (IL) techniques aim to replicate human behaviors in specific tasks. While IL has gained prominence due to its effectiveness and efficiency, traditional methods o…
Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning
Anthony Kobanda, Rémy Portelas, Odalric-Ambrym Maillard +1
We consider a Continual Reinforcement Learning setup, where a learning agent must continuously adapt to new tasks while retaining previously acquired skill sets, with a focus on th…
Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning
Alexi Canesse, Mathieu Petitbois, Ludovic Denoyer +2
Offline Reinforcement Learning (RL) has emerged as a powerful alternative to imitation learning for behavior modeling in various domains, particularly in complex navigation tasks.…
Efficient Active Imitation Learning with Random Network Distillation
Emilien Biré, Anthony Kobanda, Ludovic Denoyer +1
Developing agents for complex and underspecified tasks, where no clear objective exists, remains challenging but offers many opportunities. This is especially true in video games,…
Policy Diversity for Cooperative Agents
Mingxi Tan, Andong Tian, Ludovic Denoyer
Standard cooperative multi-agent reinforcement learning (MARL) methods aim to find the optimal team cooperative policy to complete a task. However there may exist multiple differen…
Learning Computational Efficient Bots with Costly Features
Anthony Kobanda, Valliappan C. A., Joshua Romoff +1
Deep reinforcement learning (DRL) techniques have become increasingly used in various fields for decision-making processes. However, a challenge that often arises is the trade-off…