17 citations · 28 across the 18 of their papers we have counts for
19 papers
An -Optimal Sequential Approach for Solving zs-POSGs
Erwan C. Escudie, Matthia Sabatelli, Jilles S. Dibangoye
While recent reductions of zero-sum partially observable stochastic games (zs-POSGs) to transition-independent stochastic games (TI-SGs) theoretically admit dynamic programming, pr…
On Dynamic Programming Theory for Leader-Follower Stochastic Games
Jilles Steeve Dibangoye, Thibaut Le Marre, Ocan Sankur +1
Leader-follower general-sum stochastic games (LF-GSSGs) model sequential decision-making under asymmetric commitment, where a leader commits to a policy and a follower best respond…
ε-Optimally Solving Two-Player Zero-Sum POSGs
Erwan Christian Escudie, Matthia Sabatelli, Olivier Buffet +1
We present a novel framework for ε-optimally solving two-player zero-sum partially observable stochastic games (zs-POSGs). These games pose a major challenge due to the absence of…
ICE-ID: A Novel Historical Census Dataset for Longitudinal Identity Resolution
Gonçalo Hora de Carvalho, Lazar S. Popov, Sander Kaatee +6
We introduce \textbf{ICE-ID}, a benchmark dataset comprising 984,028 records from 16 Icelandic census waves spanning 220 years (1703--1920), with 226,864 expert-curated person iden…
Optimally Solving Simultaneous-Move Dec-POMDPs: The Sequential Central Planning Approach
Johan Peralez, Aurèlien Delage, Jacopo Castellini +2
The centralized training for decentralized execution paradigm emerged as the state-of-the-art approach to -optimally solving decentralized partially observable Markov decision p…
-Optimally Solving Zero-Sum POSGs
Erwan Escudie, Matthia Sabatelli, Jilles Dibangoye
A recent method for solving zero-sum partially observable stochastic games (zs-POSGs) embeds the original game into a new one called the occupancy Markov game. This reformulation a…
Solving Hierarchical Information-Sharing Dec-POMDPs: An Extensive-Form Game Approach
Johan Peralez, Aurélien Delage, Olivier Buffet +1
A recent theory shows that a multi-player decentralized partially observable Markov decision process can be transformed into an equivalent single-player game, enabling the applicat…
On Convex Optimal Value Functions For POSGs
Rafael F. Cunha, Jacopo Castellini, Johan Peralez +1
Multi-agent planning and reinforcement learning can be challenging when agents cannot see the state of the world or communicate with each other due to communication costs, latency,…
LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction
Manuel Alejandro Diaz-Zapata, David Sierra González, Özgür Erkent +2
Semantic grids can be useful representations of the scene around an autonomous system. By having information about the layout of the space around itself, a robot can leverage this…
LAPTNet: LiDAR-Aided Perspective Transform Network
Manuel Alejandro Diaz-Zapata, Özgür Erkent, Christian Laugier +2
Semantic grids are a useful representation of the environment around a robot. They can be used in autonomous vehicles to concisely represent the scene around the car, capturing vit…
HSVI can solve zero-sum Partially Observable Stochastic Games
Aurélien Delage, Olivier Buffet, Jilles S. Dibangoye +1
State-of-the-art methods for solving 2-player zero-sum imperfect information games rely on linear programming or regret minimization, though not on dynamic programming (DP) or heur…
HSVI for zs-POSGs using Concavity, Convexity and Lipschitz Properties
Aurélien Delage, Olivier Buffet, Jilles Dibangoye
Dynamic programming and heuristic search are at the core of state-of-the-art solvers for sequential decision-making problems. In partially observable or collaborative settings (\eg…
Learning to plan with uncertain topological maps
Edward Beeching, Jilles Dibangoye, Olivier Simonin +1
We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven…
On Bellman's Optimality Principle for zs-POSGs
Olivier Buffet, Jilles Dibangoye, Aurélien Delage +2
Many non-trivial sequential decision-making problems are efficiently solved by relying on Bellman's optimality principle, i.e., exploiting the fact that sub-problems are nested rec…
EgoMap: Projective mapping and structured egocentric memory for Deep RL
Edward Beeching, Christian Wolf, Jilles Dibangoye +1
Tasks involving localization, memorization and planning in partially observable 3D environments are an ongoing challenge in Deep Reinforcement Learning. We present EgoMap, a spatia…
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
Edward Beeching, Christian Wolf, Jilles Dibangoye +1
An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understandi…
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
Quentin Debard, Jilles Steeve Dibangoye, Stéphane Canu +1
Using touch devices to navigate in virtual 3D environments such as computer assisted design (CAD) models or geographical information systems (GIS) is inherently difficult for human…
Multi-UAV Visual Coverage of Partially Known 3D Surfaces: Voronoi-based Initialization to Improve Local Optimizers
Alessandro Renzaglia, Jilles Dibangoye, Vincent Le Doze +1
In this paper we study the problem of steering a team of Unmanned Aerial Vehicles (UAVs) toward a static configuration which maximizes the visibility of a 3D environment. The UAVs…
Scaling Up Decentralized MDPs Through Heuristic Search
Jilles S. Dibangoye, Christopher Amato, Arnoud Doniec
Decentralized partially observable Markov decision processes (Dec-POMDPs) are rich models for cooperative decision-making under uncertainty, but are often intractable to solve opti…