Publications

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2022

Shangmin Guo, Yi Ren, Kory Mathewson, Simon Kirby, Stefano V. Albrecht, Kenny Smith
Expressivity of Emergent Languages is a Trade-off between Contextual Complexity and Unpredictability
International Conference on Learning Representations (ICLR), 2022
Abstract | BibTex | arXiv | Code
ICLRmulti-agent-rlemergent-communication

Lukas Schäfer, Filippos Christianos, Josiah P. Hanna, Stefano V. Albrecht
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022
Abstract | BibTex | arXiv | Code
AAMASdeep-rlintrinsic-reward

Lukas Schäfer
Task Generalisation in Multi-Agent Reinforcement Learning
International Conference on Autonomous Agents and Multiagent Systems, Doctoral Consortium (AAMAS), 2022
Abstract | BibTex | Paper
AAMASmulti-agent-rl

Filippos Christianos
Collaborative Training of Multiple Autonomous Agents
International Conference on Autonomous Agents and Multiagent Systems, Doctoral Consortium (AAMAS), 2022
Abstract | BibTex | Paper
AAMASmulti-agent-rl

Balint Gyevnar, Massimiliano Tamborski, Cheng Wang, Christopher G. Lucas, Shay B. Cohen, Stefano V. Albrecht
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning
IJCAI Workshop on Artificial Intelligence for Autonomous Driving (IJCAI), 2022
Abstract | BibTex | arXiv | Code
IJCAIautonomous-drivingexplainable-ai

Giuseppe Vecchio, Simone Palazzo, Dario C Guastella, Ignacio Carlucho, Stefano V Albrecht, Giovanni Muscato, Concetto Spampinato
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments
ICRA 2022 Workshop on Releasing Robots into the Wild: Simulations, Benchmarks, and Deployment (ICRA), 2022
Abstract | BibTex | arXiv
ICRAdeep-rlsimulator

Reuth Mirsky, Ignacio Carlucho, Arrasy Rahman, Elliot Fosong, William Macke, Mohan Sridharan, Peter Stone, Stefano V. Albrecht
A Survey of Ad Hoc Teamwork: Definitions, Methods, and Open Problems
arXiv:1801.03331, 2022
Abstract | BibTex | arXiv
surveyad-hoc-teamwork

Morris Antonello, Mihai Dobre, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles
arXiv:2203.08251, 2022
Abstract | BibTex | arXiv
autonomous-driving

Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning Representations for Control with Hierarchical Forward Models
arXiv:2206.11396, 2022
Abstract | BibTex | arXiv
deep-rl

2021

Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Conference on Neural Information Processing Systems, Datasets and Benchmarks Track (NeurIPS), 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlmulti-agent-rl

Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Conference on Neural Information Processing Systems (NeurIPS), 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlagent-modelling

Rujie Zhong, Josiah P. Hanna, Lukas Schäfer, Stefano V. Albrecht
Robust On-Policy Data Collection for Data-Efficient Policy Evaluation
NeurIPS Workshop on Offline Reinforcement Learning (NeurIPS), 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlpolicy-evaluation

Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
International Conference on Machine Learning (ICML), 2021
Abstract | BibTex | arXiv | Video | Code
ICMLdeep-rlagent-modellingad-hoc-teamwork

Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
International Conference on Machine Learning (ICML), 2021
Abstract | BibTex | arXiv | Video | Code
ICMLdeep-rlmulti-agent-rl

Lukas Schäfer, Filippos Christianos, Josiah Hanna, Stefano V. Albrecht
Decoupling Exploration and Exploitation in Reinforcement Learning
ICML Workshop on Unsupervised Reinforcement Learning (ICML), 2021
Abstract | BibTex | arXiv | Code
ICMLdeep-rlintrinsic-reward

Stefano V. Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, Subramanian Ramamoorthy
Interpretable Goal-based Prediction and Planning for Autonomous Driving
IEEE International Conference on Robotics and Automation (ICRA), 2021
Abstract | BibTex | arXiv | Video | Code
ICRAautonomous-drivinggoal-recognition

Cillian Brewitt, Balint Gyevnar, Samuel Garcin, Stefano V. Albrecht
GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
Abstract | BibTex | arXiv | Video | Code
IROSautonomous-drivinggoal-recognition

Josiah P. Hanna, Arrasy Rahman, Elliot Fosong, Francisco Eiras, Mihai Dobre, John Redford, Subramanian Ramamoorthy, Stefano V. Albrecht
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
Abstract | BibTex | arXiv
IROSautonomous-drivinggoal-recognition

Henry Pulver, Francisco Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
Abstract | BibTex | arXiv | Video
IROSautonomous-driving

Francisco Eiras, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
A Two-Stage Optimization-based Motion Planner for Safe Urban Driving
IEEE Transactions on Robotics (T-RO), 2021
Abstract | BibTex | arXiv | Publisher | Video
T-ROautonomous-driving

Ibrahim H. Ahmed, Josiah P. Hanna, Elliot Fosong, Stefano V. Albrecht
Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction
International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), 2021
Abstract | BibTex | arXiv | Publisher | Code
PAAMSsecurityagent-modelling

Shangmin Guo, Yi Ren, Kory Mathewson, Simon Kirby, Stefano V. Albrecht, Kenny Smith
Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability
arXiv:2106.03982, 2021
Abstract | BibTex | arXiv
multi-agent-rlemergent-communication

Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
arXiv:2110.04935, 2021
Abstract | BibTex | arXiv | Code
deep-rl

2020

Stefano V. Albrecht, Peter Stone, Michael P. Wellman
Special Issue on Autonomous Agents Modelling Other Agents: Guest Editorial
Artificial Intelligence (AIJ), 2020
Abstract | BibTex | Publisher | Special Issue
AIJagent-modelling

Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Conference on Neural Information Processing Systems (NeurIPS), 2020
Abstract | BibTex | arXiv
NeurIPSdeep-rlmulti-agent-rl

Georgios Papoudakis, Stefano V. Albrecht
Variational Autoencoders for Opponent Modeling in Multi-Agent Systems
AAAI Workshop on Reinforcement Learning in Games (AAAI), 2020
Abstract | BibTex | arXiv
AAAIdeep-rlagent-modelling

Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht
Open Ad Hoc Teamwork using Graph-based Policy Learning
arXiv:2006.10412, 2020
Abstract | BibTex | arXiv
deep-rlagent-modellingad-hoc-teamwork

Georgios Papoudakis, Filippos Christianos , Lukas Schäfer, Stefano V. Albrecht
Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms
arXiv:2006.07869, 2020
Abstract | BibTex | arXiv
deep-rlmulti-agent-rl

Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
Local Information Opponent Modelling Using Variational Autoencoders
arXiv:2006.09447, 2020
Abstract | BibTex | arXiv
deep-rlagent-modelling

Ibrahim H. Ahmed, Josiah P. Hanna, Stefano V. Albrecht
Quantum-Secure Authentication via Abstract Multi-Agent Interaction
arXiv:2007.09327, 2020
Abstract | BibTex | arXiv
securityagent-modelling

Stefano V. Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, Subramanian Ramamoorthy
Interpretable Goal-based Prediction and Planning for Autonomous Driving
arXiv:2002.02277, 2020
Abstract | BibTex | arXiv
autonomous-drivinggoal-recognition

Henry Pulver, Francisco Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
arXiv:2011.00509, 2020
Abstract | BibTex | arXiv
autonomous-driving

Francisco Eiras, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
Two-Stage Optimization-based Motion Planner for Safe Urban Driving
arXiv:2002.02215, 2020
Abstract | BibTex | arXiv
autonomous-driving

2019

Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom
Stabilizing Generative Adversarial Networks: A Survey
arXiv:1910.00927, 2019
Abstract | BibTex | arXiv
surveysecuritygan

Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
arXiv:1906.04737, 2019
Abstract | BibTex | arXiv
surveydeep-rlmulti-agent-rl

2018

Stefano V. Albrecht, Peter Stone
Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
Artificial Intelligence (AIJ), 2018
Abstract | BibTex | arXiv | Publisher
AIJsurveyagent-modellinggoal-recognition

Craig Innes, Alex Lascarides, Stefano V. Albrecht, Subramanian Ramamoorthy, Benjamin Rosman
Reasoning about Unforeseen Possibilities During Policy Learning
arXiv:1801.03331, 2018
Abstract | BibTex | arXiv
causal

2017

Stefano V. Albrecht, Somchaya Liemhetcharat, Peter Stone
Special Issue on Multiagent Interaction without Prior Coordination: Guest Editorial
Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2017
Abstract | BibTex | Publisher | MIPC Workshop Series
JAAMASad-hoc-teamwork

Stefano V. Albrecht, Peter Stone
Reasoning about Hypothetical Agent Behaviours and their Parameters
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2017
Abstract | BibTex | arXiv
AAMASad-hoc-teamworkagent-modelling

Stefano V. Albrecht, Subramanian Ramamoorthy
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
International Joint Conference on Artificial Intelligence (IJCAI), 2017
Abstract | BibTex | arXiv
IJCAIstate-estimationcausal

2016

Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
Belief and Truth in Hypothesised Behaviours
Artificial Intelligence (AIJ), 2016
Abstract | BibTex | arXiv | Publisher
AIJad-hoc-teamworkagent-modelling

Stefano V. Albrecht, Subramanian Ramamoorthy
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Journal of Artificial Intelligence Research (JAIR), 2016
Abstract | BibTex | arXiv | Publisher
JAIRstate-estimationcausal

2015

Stefano V. Albrecht, Subramanian Ramamoorthy
Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
Conference on Uncertainty in Artificial Intelligence (UAI), 2015
Abstract | BibTex | arXiv
UAIagent-modelling

Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types
AAAI Conference on Artificial Intelligence (AAAI), 2015
Abstract | BibTex | arXiv | Appendix
AAAIagent-modelling

Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
E-HBA: Using Action Policies for Expert Advice and Agent Typification
AAAI Workshop on Multiagent Interaction without Prior Coordination (AAAI), 2015
Abstract | BibTex | arXiv | Appendix
AAAIad-hoc-teamworkagent-modelling

2014

Stefano V. Albrecht, Subramanian Ramamoorthy
On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
Conference on Uncertainty in Artificial Intelligence (UAI), 2014
Abstract | BibTex | arXiv | Appendix
UAIagent-modelling

2013

Stefano V. Albrecht, Subramanian Ramamoorthy
A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2013
Abstract | BibTex | arXiv (full technical report) | Extended Abstract
AAMASad-hoc-teamworkagent-modelling

2012

Stefano V. Albrecht, Subramanian Ramamoorthy
Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2012
Abstract | BibTex | arXiv
AAMASmulti-agent-rlad-hoc-teamwork