31 citations · 35 across the 11 of their papers we have counts for
16 papers
Mobile Network Control with a World Model
Maxime Bouton, Ioanna Mitsioni, Simon Lindståhl +1
The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management. We propose a world model-based app…
Temporally Consistent Graph Q-Networks for Intelligent Network Control
Zacharias Veiksaar, Maxime Bouton
Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services. As network complexity r…
Collaborative Safe Bayesian Optimization
Alina Castell Blasco, Maxime Bouton
Mobile networks require safe optimization to adapt to changing conditions in traffic demand and signal transmission quality, in addition to improving service performance metrics. W…
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
Haneya Naeem Qureshi, Usama Masood, Marvin Manalastas +8
The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshoo…
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning
Maxime Bouton, Jaeseong Jeong, Jose Outes +2
Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters. Optimizing these parameters is necessary for ensur…
Model Based Residual Policy Learning with Applications to Antenna Control
Viktor Eriksson Möllerstedt, Alessio Russo, Maxime Bouton
Non-differentiable controllers and rule-based policies are widely used for controlling real systems such as telecommunication networks and robots. Specifically, parameters of mobil…
A Graph Attention Learning Approach to Antenna Tilt Optimization
Yifei Jin, Filippo Vannella, Maxime Bouton +2
6G will move mobile networks towards increasing levels of complexity. To deal with this complexity, optimization of network parameters is key to ensure high performance and timely…
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…
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura +1
Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participa…
Decomposition Methods with Deep Corrections for Reinforcement Learning
Maxime Bouton, Kyle Julian, Alireza Nakhaei +2
Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility…
Belief State Planning for Autonomously Navigating Urban Intersections
Maxime Bouton, Akansel Cosgun, Mykel J. Kochenderfer
Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rap…