Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Toromanoff, Marin"'
Autor:
Chekroun, Raphael, Wang, Han, Lee, Jonathan, Toromanoff, Marin, Hornauer, Sascha, Moutarde, Fabien, Monache, Maria Laura Delle
Accurate real-time traffic state forecasting plays a pivotal role in traffic control research. In particular, the CIRCLES consortium project necessitates predictive techniques to mitigate the impact of data source delays. After the success of the Meg
Externí odkaz:
http://arxiv.org/abs/2402.05663
We present MBAPPE, a novel approach to motion planning for autonomous driving combining tree search with a partially-learned model of the environment. Leveraging the inherent explainable exploration and optimization capabilities of the Monte-Carlo Se
Externí odkaz:
http://arxiv.org/abs/2309.08452
Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stabil
Externí odkaz:
http://arxiv.org/abs/2111.08575
Reinforcement Learning (RL) aims at learning an optimal behavior policy from its own experiments and not rule-based control methods. However, there is no RL algorithm yet capable of handling a task as difficult as urban driving. We present a novel te
Externí odkaz:
http://arxiv.org/abs/1911.10868
Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to v
Externí odkaz:
http://arxiv.org/abs/1908.04683
Autor:
Toromanoff, Marin, Wirbel, Emilie, Wilhelm, Frédéric, Vejarano, Camilo, Perrotton, Xavier, Moutarde, Fabien
Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failure cases. In this paper we present a novel model to generate this data
Externí odkaz:
http://arxiv.org/abs/1808.06940
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly proposed reward and learning strategies lead together to faster converge
Externí odkaz:
http://arxiv.org/abs/1807.02371
Publikováno v:
Robotics; Oct2023, Vol. 12 Issue 5, p127, 13p
Autor:
Toromanoff, Marin
Publikováno v:
Robotique [cs.RO]. Université Paris sciences et lettres, 2021. Français. ⟨NNT : 2021UPSLM020⟩
In this thesis, we address the challenges of autonomous driving in an urban environment using end-to-end deep reinforcement learning algorithms. Reinforcement learning (RL) is one of the three paradigms of machine learning. It distinguishes itself fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d696cc12c25fc0cb01334343e9e0205e
https://pastel.archives-ouvertes.fr/tel-03347567
https://pastel.archives-ouvertes.fr/tel-03347567
Publikováno v:
Deep Reinforcement Learning Workshop of 39th Conference on Neural Information Processing Systems (Neurips'2019)
Deep Reinforcement Learning Workshop of 39th Conference on Neural Information Processing Systems (Neurips'2019), Dec 2019, Vancouver, Canada
Deep Reinforcement Learning Workshop of 39th Conference on Neural Information Processing Systems (Neurips'2019), Dec 2019, Vancouver, Canada
International audience; Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2592::26dbee32d4ed10832348b58cf9a0adfa
https://hal-mines-paristech.archives-ouvertes.fr/hal-02368263
https://hal-mines-paristech.archives-ouvertes.fr/hal-02368263