197 citations · 234 across the 23 of their papers we have counts for
25 papers
Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders
Victor-Alexandru Darvariu, Charlotte Z. Reed, Jan Stratmann +21
Underwater glider robots have become indispensable for ocean sampling, yet fully autonomous long-term operation remains rare in practice. Although stakeholders are calling for tool…
Improving Regret Approximation for Unsupervised Dynamic Environment Generation
Harry Mead, Bruno Lacerda, Jakob Foerster +1
Unsupervised Environment Design (UED) seeks to automatically generate training curricula for reinforcement learning (RL) agents, with the goal of improving generalisation and zero-…
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…
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…
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…
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
Alexander Rutherford, Michael Beukman, Timon Willi +3
What data or environments to use for training to improve downstream performance is a longstanding and very topical question in reinforcement learning. In particular, Unsupervised E…
Monte Carlo Tree Search with Boltzmann Exploration
Michael Painter, Mohamed Baioumy, Nick Hawes +1
Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques. However, UCT can be slow to explor…
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally,…
A Framework for Learning from Demonstration with Minimal Human Effort
Marc Rigter, Bruno Lacerda, Nick Hawes
We consider robot learning in the context of shared autonomy, where control of the system can switch between a human teleoperator and autonomous control. In this setting we address…
Formal Modelling for Multi-Robot Systems Under Uncertainty
Charlie Street, Masoumeh Mansouri, Bruno Lacerda
Purpose of Review: To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution. In this paper, w…
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
Offline reinforcement learning (RL) is suitable for safety-critical domains where online exploration is too costly or dangerous. In such safety-critical settings, decision-making s…
RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
Offline reinforcement learning (RL) aims to find performant policies from logged data without further environment interaction. Model-based algorithms, which learn a model of the en…
Planning for Risk-Aversion and Expected Value in MDPs
Marc Rigter, Paul Duckworth, Bruno Lacerda +1
Planning in Markov decision processes (MDPs) typically optimises the expected cost. However, optimising the expectation does not consider the risk that for any given run of the MDP…
Risk-Aware Motion Planning in Partially Known Environments
Fernando S. Barbosa, Bruno Lacerda, Paul Duckworth +2
Recent trends envisage robots being deployed in areas deemed dangerous to humans, such as buildings with gas and radiation leaks. In such situations, the model of the underlying ha…
On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference
Mohamed Baioumy, Bruno Lacerda, Paul Duckworth +1
Previous work on planning as active inference addresses finite horizon problems and solutions valid for online planning. We propose solving the general Stochastic Shortest-Path Mar…
Risk-Averse Bayes-Adaptive Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
In this work, we address risk-averse Bayes-adaptive reinforcement learning. We pose the problem of optimising the conditional value at risk (CVaR) of the total return in Bayes-adap…
Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processes
Marc Rigter, Bruno Lacerda, Nick Hawes
The parameters for a Markov Decision Process (MDP) often cannot be specified exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets which the parameters belo…
Active Inference for Integrated State-Estimation, Control, and Learning
Mohamed Baioumy, Paul Duckworth, Bruno Lacerda +1
This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent…
Convex Hull Monte-Carlo Tree Search
Michael Painter, Bruno Lacerda, Nick Hawes
This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives. We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework,…
Simultaneous Task Allocation and Planning Under Uncertainty
Fatma Faruq, Bruno Lacerda, Nick Hawes +1
We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments. Task allocation is performed simultaneously with planning,…