Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Matignon, Laetitia"'
Successfully addressing a wide variety of tasks is a core ability of autonomous agents, requiring flexibly adapting the underlying decision-making strategies and, as we argue in this work, also adapting the perception modules. An analogical argument
Externí odkaz:
http://arxiv.org/abs/2402.07739
Learning robot navigation strategies among pedestrian is crucial for domain based applications. Combining perception, planning and prediction allows us to model the interactions between robots and pedestrians, resulting in impressive outcomes especia
Externí odkaz:
http://arxiv.org/abs/2401.17914
Autor:
Marza, Pierre, Matignon, Laetitia, Simonin, Olivier, Batra, Dhruv, Wolf, Christian, Chaplot, Devendra Singh
Implicit representations such as Neural Radiance Fields (NeRF) have been shown to be very effective at novel view synthesis. However, these models typically require manual and careful human data collection for training. In this paper, we present Auto
Externí odkaz:
http://arxiv.org/abs/2304.11241
Understanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SLAM variants, which maintain a topological or metric representation, en
Externí odkaz:
http://arxiv.org/abs/2210.05129
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a number of scientific and technical challenges still need to be resolved
Externí odkaz:
http://arxiv.org/abs/2209.08890
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals. This ability can be associated with spatial reasoning
Externí odkaz:
http://arxiv.org/abs/2107.06011
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states. Following this,we introduce DisTop, a new model that simultaneously learns diverse skills and focuses
Externí odkaz:
http://arxiv.org/abs/2106.03853
Taking inspiration from developmental learning, we present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to-end way. With this architecture, an agent focuses only on task-reward
Externí odkaz:
http://arxiv.org/abs/2006.12903
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a number of scientific and technical challenges still need to be addresse
Externí odkaz:
http://arxiv.org/abs/1908.06976
This paper presents a new algorithm: TSRuleGrowth, looking for partially-ordered rules over a time series. This algorithm takes principles from the state of the art of rule mining and applies them to time series via a new notion of support. We apply
Externí odkaz:
http://arxiv.org/abs/1907.10054