31 citations · 33 across the 5 of their papers we have counts for
8 papers
Coordinated Reinforcement Learning for Optimizing Mobile Networks
Maxime Bouton, Hasan Farooq, Julien Forgeat +3
Mobile networks are composed of many base stations and for each of them many parameters must be optimized to provide good services. Automatically and dynamically optimizing all the…
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
Maxime Bouton, Alireza Nakhaei, David Isele +2
Maneuvering in dense traffic is a challenging task for autonomous vehicles because it requires reasoning about the stochastic behaviors of many other participants. In addition, the…
Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes
Maxime Bouton, Jana Tumova, Mykel J. Kochenderfer
Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the s…
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura +1
Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving…
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
Maxime Bouton, Jesper Karlsson, Alireza Nakhaei +3
Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain…
Pedestrian Collision Avoidance System for Scenarios with Occlusions
Markus Schratter, Maxime Bouton, Mykel J. Kochenderfer +1
Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches r…