Stefano V. Albrecht

Dr. Stefano V. Albrecht

Reader (Associate Professor) in Artificial Intelligence

Research Group Leader

Stefano V. Albrecht's publications | Personal page

Postdoctoral Researchers

Cheng Wang

Dr. Cheng Wang

PhD in Engineering, Technical University of Darmstadt, 2021; MS in Automotive Engineering, Tongji University, 2017; BS in Automotive Engineering, Wuhan University of Technology, 2014

Project: Asking Your Autonomous Car to Explain its Decisions: Towards AI That Explains Itself

Research interests: prediction and planning, simulation and testing, safety verification and validation, autonomous vehicles

Cheng Wang's publications | Personal page

Dongge Han

Dr. Dongge Han

PhD in Computer Science, University of Oxford, 2022; MSc in Computer Science, University of Oxford, 2016; BSc in Physics, The Hong Kong University of Science and Technology, 2015

Project: Multimodal Integration for Sample-Efficient Deep Reinforcement Learning

Research interests: reinforcement learning, recommender systems, multi-agent systems

Dongge Han's publications | Personal page

Atish Dixit

Dr. Atish Dixit

PhD in Engineering, Heriot-Watt University, 2023; MTech in Mathematical Modelling, 2017 & BEng in Mechanical Engineering, 2012, University of Pune

Project: Multi-Agent Reinforcement Learning and Ad Hoc Multi-Agent Collaboration

Research interests: reinforcement learning, multi-agent systems, robotics, intelligent control

Atish Dixit's publications

Josiah P. Hanna

Dr. Josiah P. Hanna (Jan 2019 – Jul 2020)

PhD in Computer Science, University of Texas at Austin, 2019; BS in Computer Science and Mathematics, University of Kentucky, 2014

Project: Towards Model Criticism in Multi-Agent Systems

Research interests: reinforcement learning, policy evaluation, robotics, autonomous driving

Josiah P. Hanna's publications | Personal page

Ignacio Carlucho

Dr. Ignacio Carlucho (Nov 2021 – April 2023)

PhD in Engineering, National University of Central Buenos Aires, 2019; BS in Electromechanical Engineering, National University of Central Buenos Aires, 2015

Project: Explainable Reasoning, Learning and Ad hoc Multi-agent Collaboration

Research interests: reinforcement learning, multi-agent systems, robotics, intelligent control

Ignacio Carlucho's publications | Personal page

PhD Research Students

Georgios Papoudakis

Georgios Papoudakis

Diploma in Electrical and Computer Engineering, Aristotle University of Thessaloniki, 2017

Project: Modelling in Multi-Agent Systems Using Representation Learning

Multi-agent systems in partially observable environments face many challenging problems which traditional reinforcement learning algorithms fail to address. Agents have to deal with the lack of information about the environment's state and the opponents' beliefs and goals. A promising research direction is to learn models of the other agents to better understand their interactions. This project will investigate representation learning for opponent modelling in order to improve learning in multi-agent systems.

Georgios Papoudakis's publications | Personal page

Elliot Fosong

Elliot Fosong

BA & MEng in Engineering, University of Cambridge, 2019

Project: Coordination of Pre-skilled Agents to Complete Unseen Tasks

Teams of autonomous agents can be trained to complete specific desirable tasks. When a new task arises, we might wish to form a new team to complete this task by selecting existing agents whose skills could be useful to solve the new task. Despite the agents' individual skills in respective roles, the newly formed team needs to learn to coordinate to solve the new task. This project aims to develop methods by which the already-skilled agents can learn to cooperate to solve a new task, given a limited number of 'trial runs' on the new task.

Elliot Fosong's publications | Personal page

Lukas Schäfer

Lukas Schäfer

MSc Informatics, University of Edinburgh, 2019; BSc Computer Science, Saarland University, 2018

Project: Sample Efficiency and Generalisation in Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning systems often require many millions of interactions to learn complex behaviour. Furthermore, the learned behaviour usually lacks generalisation ability. These challenges of sample efficiency and generalisation together severely limit the possible application of multi-agent reinforcement learning. This project will leverage distributed information and the multi-agent nature of such systems to enable agents to learn effective behaviours with less data and be able to learn robust, re-usable skills which transfer to new environments.

Lukas Schäfer's publications | Personal page

Mhairi Dunion

Mhairi Dunion

BSc (Hons) Mathematics, University of Edinburgh, 2013

Project: Causality in Deep Reinforcement Learning

A challenge of deep reinforcement learning is that it does not generalise to unseen tasks with the same underlying dynamics because it overfits to the training task. In practice, it is common to train algorithms with random initialisations of all environment variables to maximise the tasks seen during training, which is not pragmatic or sample efficient. This project will investigate novel methods to improve generalisation to unseen tasks by combining causal inference techniques with deep reinforcement learning because causal relationships remain invariant to the change in task.

Mhairi Dunion's publications

Shangmin Guo

Shangmin Guo

MSc Data Science, University of Edinburgh, 2019; BE Computer Science, China National University of Defense Technology, 2014

Project: Deep Iterated Learning

With the development of human society, natural languages have greatly evolved such that people can easily interpret meaning of novel phrases with the help from compositionality. During its evolution, languages have been shaped by repeated learning, interacting and transmitting, which are referred to as iterated learning. Considering language as a special kind of representation, this project aims to explore whether and how the same learning mechanism could be applied to deep learning (thus deep iterated learning) and whether it can help to improve the generalisation of multi-agent systems, and machine learning systems overall.

Shangmin Guo's publications

Samuel Garcin

Samuel Garcin

MEng in Aeronautical Engineering, Imperial College London, 2018

Project: Adaptive Curriculum Design for Generalisation in Deep Reinforcement Learning

A key limitation preventing the wider adoption of Deep Reinforcement Learning (DRL) today is its difficulty generalising to environments or tasks which were not encountered during training. The frameworks put forward to tackle this issue, such as Meta Reinforcement Learning or Representation Learning, primarily focus on the DRL agent and do not act on the training task generation process. This project will investigate how Representation Learning methods may be employed to learn a structured latent representation of the problem class to be solved, enabling the generation of an adaptive task distribution that captures the problem class, and is adapted to the current level of ability of the agent.

Samuel Garcin's publications

Balint Gyevnar

Balint Gyevnar

MInf Informatics, University of Edinburgh, 2021

Project: Natural Language Explanations for Autonomous Vehicle Motion Planning and Prediction

Achieving trust and safety for autonomous vehicles is critical to their public success. However, most current methods rely on opaque and unaccountable black-box algorithms, making their legal and social adoption difficult. Instead, grounded in interpretable and explainable methods such as IGP2 and integrated with natural language processing and cognitive modelling, my project will investigate how to generate and deliver the most relevant and intelligible explanations for users, with the end goal of building trust and transparency in autonomous vehicles.

Balint Gyevnar's publications | Personal page

Trevor McInroe

Trevor McInroe

MS Artificial Intelligence, Northwestern University, 2022; BBA Economics, University of North Texas, 2017

Project: Enabling Real-World Offline Reinforcement Learning with Representation Learning

The sample complexity of reinforcement learning (RL) algorithms hinders their application to real-world systems. This inefficiency is exacerbated by high-dimensional state spaces, such as those composed of pixels. My research will investigate how representation learning routines can disentangle useful information from small offline datasets of robotic tasks. My ultimate goal is to make RL feasible for the average industry group in the same way that computer vision has become accessible over the past decade.

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Sabrina McCallum-Exner

Sabrina McCallum-Exner

MSc Artificial Intelligence, University of Strathclyde, 2022; BA Business Administration, Berlin School of Economics and Law, 2015

Project: Learning Grounded Representations from Multi-Modal Feedback and Interactions with Embodied Environments

Learning complex, hierarchical tasks or diverse, open-ended tasks when rewards are sparse or there is no clear success criterion remains a challenge for RL agents. Manually crafting dense shaping rewards is non-trivial and even potentially infeasible for some environments, and choosing a good heuristic requires domain knowledge, typically resulting in task- and environment-specific solutions. This project explores alternative approaches which instead leverage information-rich language feedback and other multi-modal signals resulting directly from interactions of embodied agents with their environment.

Sabrina McCallum-Exner's publications

Raul Steleac

Raul Steleac

MSc in Computing (Artificial Intelligence and Machine Learning), Imperial College London, 2021

Project: Continual Multi-Agent Reinforcement Learning

Addressing the challenge of adaptability in multi-agent reinforcement learning, this project aims to improve agents' abilities to cooperate with diverse and changing teammates after deployment. By exposing the learner to various team configurations during training, current approaches assume that sufficient adaptability is instilled in the agent to develop a general enough cooperation capacity; however, these methods often fail to cover the entire teammate policy space in complex scenarios. This project introduces Continual Multi-Agent Reinforcement Learning to extend learning capabilities of agents beyond the initial training period, allowing them to effectively adjust to previously unseen team configurations and strategies.

Raul Steleac's publications

Kale-ab Tessera

Kale-ab Tessera

MSc Computer Science, University of the Witwatersrand, 2021; BSc Hons in Computer Science, University of Pretoria, 2016

Project: Scalable Coordination in Multi-Agent Reinforcement Learning

In Multi-Agent Reinforcement Learning (MARL), scalable coordination has historically been a significant challenge, limiting the real-world applicability of these algorithms. With the increasing deployment of machine learning systems, the need for scalable MARL methods that can interact with diverse agents becomes even more crucial. Our research focuses on improving the scalability of MARL algorithms, while also ensuring these algorithms can adapt to seen and unseen agents.

Kale-ab Tessera's publications | Personal page

Elle Miller

Elle Miller

Bachelor of Mechatronic (Space) Engineering & Bachelor of Advanced Science (Physics), University of Sydney, 2023

Project: Deep Reinforcement Learning for Safe and Compliant Human-Robot Interaction

Robots possess significant potential to enhance the quality of life for individuals with disabilities, support healthcare professionals, and provide care to an ageing population. To help with tasks such as drinking, dressing, and personal hygiene, robots will need to perform very intricate behaviours while in direct physical contact with humans. Deep Reinforcement Learning (DRL) has emerged as a promising avenue to acquire diverse complex behaviours safely through simulation. However, an open challenge lies in transferring these learned policies to real-world scenarios while ensuring safety. In this project, we investigate the potential of DRL to learn safe and compliant assistive behaviours for physical human-robot interaction.

Elle Miller's publications | Personal page

Leonard Hinckeldey

Leonard Hinckeldey

MSc Applied Social Data Science, London School of Economics, 2023; BSc Economics, SOAS University of London, 2022

Project: Collaborative Multi-Agent Reinforcement Learning for Ad-Hoc Human-AI Teams

Multi-Agent Reinforcement Learning (MARL) holds great potential for coordinating the behaviour of artificial agents in complex real-world environments. However, MARL agents often perform poorly when partnered with agents previously unseen during training, which hampers their deployment in real-world environments that involve human agents. This project explores how MARL algorithms can learn generalisable policies, enabling effective and spontaneous collaboration between RL agents and humans.

Leonard Hinckeldey's publications

Ibrahim H. Ahmed

Ibrahim H. Ahmed (Sep 2018 – May 2022)

MS in Computer Science, UC Davis, 2018; BS in Computer Science, UC Davis, 2016

Project: Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction

Authentication and key establishment are the foundation for secure communication over computer networks. However, modern protocols which rely on public key cryptography for secure communication are vulnerable to quantum technology–based attacks. My project studies a novel quantum-safe method for authentication and key establishment based on abstract multi-agent interaction. It introduces these fields to multi-agent techniques for optimisation and rational decision-making.

Ibrahim H. Ahmed's publications

Arrasy Rahman

Arrasy Rahman (Sep 2018 – Apr 2023)

MSc Data Science, University of Edinburgh, 2017; BSc Computer Science, Universitas Indonesia, 2015

Project: Ad Hoc Teamwork in Open Multi-Agent Systems using Graph Neural Networks

Many real-world problems require an agent to achieve specific goals by interacting with other agents, without having predefined coordination protocols with other agents. Prior work on ad hoc teamwork focused on multi-agent systems in which the number of agents is assumed fixed. My project focuses on using Graph Neural Networks (GNNs) to handle interaction data between varying number of agents. We explore the possibility of combining GNNs with Reinforcement Learning techniques to implement agents that can perform well in teams with dynamic composition

Arrasy Rahman's publications

Filippos Christianos

Filippos Christianos (Sep 2018 – Jun 2023)

Diploma in Electronic and Computer Engineering, Technical University of Crete, 2017

Project: Coordinated Exploration in Multi-Agent Deep Reinforcement Learning

In the increasingly large state space encountered in deep reinforcement learning, exploration plays a critical role by narrowing down the search for an optimal policy. In multi-agent settings, the joint action space also grows exponentially, further complicating the search. The use of a partially centralized policy while exploring can coordinate the exploration and more easily locate promising, even decentralized, policies. In this project, we investigate how the coordination of agents in the exploration phase can improve the performance of deep reinforcement learning algorithms.

Filippos Christianos's publications | Personal page

Cillian Brewitt

Cillian Brewitt (Jan 2019 – May 2023)

MSc Artificial Intelligence, University of Edinburgh, 2017; BE Electrical and Electronic Engineering, University College Cork, 2016

Project: Interpretable Planning and Prediction for Autonomous Vehicles

Accurately predicting the intentions and actions of other road users and then using this information during motion planning is an important task in the field of autonomous driving. It is desirable for planning and prediction methods to be fast, accurate, interpretable, and verifiable, however current methods fail to achieve all these objectives. During this project novel methods for prediction and planning which satisfy these objectives will be investigated. My current focus is investigating how decision trees can be used for vehicle goal recognition.

Cillian Brewitt's publications

Visiting Researchers

Xuehui Yu

Xuehui Yu

BE Computer Science, Harbin Engineering University, 2019

Project: Generalisation in Reinforcement Learning via Causal Inference

Reinforcement Learning (RL) has proven to be an indispensable tool for training successful agents on difficult sequential decision-making problems. Most of these early successes focus on a fixed task in a fixed environment. However, in real applications, we often have changing environments, and RL agents are often unable to generalise well due to overfitting to their training environments. Such diverse, dynamic, and unpredictable environments make great demands on RL agents to reuse experience and adapt quickly. To improve generalization to unseen tasks, this project combines causal inference techniques with RL, enabling RL agents to understand the real world from a causal perspective and quickly adapt to out-of-distribution domains.

Xuehui Yu's publications | Personal page

Maciej Wiatrak

Maciej Wiatrak (Sep 2019 – Nov 2019)

BASc Mathematics & Computer Science, University College London, 2019

Project: Stabilising Generative Adversarial Networks with Multi-Agent Reinforcement Learning

Generative Adversarial Networks (GANs) are a state-of-the-art machine learning method, however, the training of GANs suffers from instability problems such as oscillatory behaviour and vanishing gradients. In this project, we outline the connections between GANs and Multi-Agent Reinforcement Learning (MARL), which is concerned with stable concurrent learning of multiple actors toward an equilibrium solution. We explore the connection between GANs and MARL, by proposing a GAN training method that utilises an established MARL technique based on variable learning rates.

Maciej Wiatrak's publications | Personal page

Giuseppe Vecchio

Giuseppe Vecchio (Feb 2022 – April 2022)

MSc in Computer Engineering, UNICT, 2020; BSc in Computer Engineering, UNICT, 2018

Project: Imitation Learning for Autonomous Robot Navigation in Unstructured Environments

Mobile robots have become part of everyday life in many forms like service robots, planetary rovers, autonomous cars and more. For ground robots the ability to navigate the surrounding environment is essential for long-term operations in real-world scenarios and is highly dependent on the ability to quickly adapt to new unseen settings. This project will explore the use of Imitation Learning-based techniques in simulation environments for autonomous navigation in unstructured settings and the use of domain adaptation approaches for real-world applications.

Giuseppe Vecchio's publications | Personal page

Alain Andres Fernandez

Alain Andres Fernandez (June 2022 – Aug 2022)

MSc Telecommunication Engineering, University of the Basque Country, 2019; BSc Telecommunication Engineering, University of the Basque Country, 2017

Project: Exploration under Sparse Rewards with Deep Reinforcement Learning

Reinforcement learning algorithms are highly dependable on the feedback signals (rewards) provided by the environment. Unfortunately, the design of a suitable reward function is not always trivial and the adoption of sparse signals is a common choice to determine whether the task has been accomplished. However, such sparse reward signals lead to difficult exploration challenges. This project will investigate the adoption of Imitation Learning and Intrinsic Motivation solutions to enhance exploration and improve sample efficiency in challenging sparse-reward environments.

Alain Andres Fernandez's publications | Personal page

Mahdi Kazemi Moghaddam

Mahdi Kazemi Moghaddam (Dec 2022 – Feb 2023)

Hons. Degree of Bachelor of Computer Science, University of Adelaide, 2019; BSc Electrical Engineering, Electronics, Amirkabir University of Technology, 2015

Project: Fairness and Social Welfare in Autonomous Driving

Most of the current autonomous driving approaches based on reinforcement learning focus on maximising some notion of performance (e.g. travel time) or incorporating driving safety. As such, less attention has been paid to the social welfare and fairness aspects of the interaction of such trained policies with other road users. Addressing that limitation is especially challenging in scenarios where the interest of one agent is at odds with that of others (e.g. who should leave the intersection first). In this project, we aim to train a population of autonomous vehicles which can achieve high individual performance while cooperating with other agents to maximise the overall social welfare and fairness.

Mahdi Kazemi Moghaddam's publications | Personal page