19 citations · 37 across the 22 of their papers we have counts for
9 papers · 1 filter
JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning
Ufuk Çakır, Victor-Alexandru Darvariu, Bruno Lacerda +1
Artificial intelligence methods are increasingly being explored for managing wildfires and other natural hazards. In particular, reinforcement learning (RL) is a promising path tow…
Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions
Junlin Xiao, Victor-Alexandru Darvariu, Bruno Lacerda +1
Monte Carlo Tree Search is a cornerstone algorithm for online planning, and its root-parallel variant is widely used when wall clock time is limited but best performance is desired…
Neural Value Iteration
Yang You, Ufuk Çakır, Alex Schutz +1
The value function of a POMDP exhibits the piecewise-linear-convex (PWLC) property and can be represented as a finite set of hyperplanes, known as -vectors. Most state-of-the-ar…
Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning
Alex Schutz, Victor-Alexandru Darvariu, Efimia Panagiotaki +2
Neural algorithmic reasoning (NAR) is a paradigm that trains neural networks to execute classic algorithms by supervised learning. Despite its successes, important limitations rema…
Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics
Yang You, Alex Schutz, Zhikun Li +3
Many high-level multi-agent planning problems, including multi-robot navigation and path planning, can be effectively modeled using deterministic actions and observations. In this…
Partially Observable Monte-Carlo Graph Search
Yang You, Vincent Thomas, Alex Schutz +3
Currently, large partially observable Markov decision processes (POMDPs) are often solved by sampling-based online methods which interleave planning and execution phases. However,…
A Finite-State Controller Based Offline Solver for Deterministic POMDPs
Alex Schutz, Yang You, Matias Mattamala +3
Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and…
Return Capping: Sample-Efficient CVaR Policy Gradient Optimisation
Harry Mead, Clarissa Costen, Bruno Lacerda +1
When optimising for conditional value at risk (CVaR) using policy gradients (PG), current methods rely on discarding a large proportion of trajectories, resulting in poor sample ef…
Ro-To-Go! Robust Reactive Control with Signal Temporal Logic
Roland Ilyes, Lara Brudermüller, Nick Hawes +1
Signal Temporal Logic (STL) robustness is a common objective for optimal robot control, but its dependence on history limits the robot's decision-making capabilities when used in M…