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surveydeep-rlmulti-agent-rlagent-modellingad-hoc-teamworkautonomous-drivinggoal-recognitionexplainable-aicausalgeneralisationsecurityemergent-communicationiterated-learningintrinsic-rewardsimulatorstate-estimationdeep-learningtransfer-learning
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Filippos-Christianosagent-modelling
2022
Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht
Deep Reinforcement Learning for Multi-Agent Interaction
AI Communications, 2022
Abstract | BibTex | arXiv | Publisher
AICsurveydeep-rlmulti-agent-rlad-hoc-teamworkagent-modellinggoal-recognitionsecurityexplainable-aiautonomous-driving
Abstract:
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms for autonomous systems control, with a specific focus on deep reinforcement learning and multi-agent reinforcement learning. Research problems include scalable learning of coordinated agent policies and inter-agent communication; reasoning about the behaviours, goals, and composition of other agents from limited observations; and sample-efficient learning based on intrinsic motivation, curriculum learning, causal inference, and representation learning. This article provides a broad overview of the ongoing research portfolio of the group and discusses open problems for future directions.
@article{albrecht2022aic,
author = {Ahmed, Ibrahim H. and Brewitt, Cillian and Carlucho, Ignacio and Christianos, Filippos and Dunion, Mhairi and Fosong, Elliot and Garcin, Samuel and Guo, Shangmin and Gyevnar, Balint and McInroe, Trevor and Papoudakis, Georgios and Rahman, Arrasy and Schäfer, Lukas and Tamborski, Massimiliano and Vecchio, Giuseppe and Wang, Cheng and Albrecht, Stefano V.},
title = {Deep Reinforcement Learning for Multi-Agent Interaction},
journal = {AI Communications, Special Issue on Multi-Agent Systems Research in the UK},
year = {2022}
}
2021
Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Conference on Neural Information Processing Systems, 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlagent-modelling
Abstract:
Modelling the behaviours of other agents is essential for understanding how agents interact and making effective decisions. Existing methods for agent modelling commonly assume knowledge of the local observations and chosen actions of the modelled agents during execution. To eliminate this assumption, we extract representations from the local information of the controlled agent using encoder-decoder architectures. Using the observations and actions of the modelled agents during training, our models learn to extract representations about the modelled agents conditioned only on the local observations of the controlled agent. The representations are used to augment the controlled agent's decision policy which is trained via deep reinforcement learning; thus, during execution, the policy does not require access to other agents' information. We provide a comprehensive evaluation and ablations studies in cooperative, competitive and mixed multi-agent environments, showing that our method achieves significantly higher returns than baseline methods which do not use the learned representations.
@inproceedings{papoudakis2021local,
title={Agent Modelling under Partial Observability for Deep Reinforcement Learning},
author={Georgios Papoudakis and Filippos Christianos and Stefano V. Albrecht},
booktitle = {Proceedings of the Neural Information Processing Systems (NeurIPS)},
year = {2021}
}
Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
International Conference on Machine Learning, 2021
Abstract | BibTex | arXiv | Video | Code
ICMLdeep-rlagent-modellingad-hoc-teamwork
Abstract:
Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with teammates without prior coordination mechanisms, including joint training. Prior work in this area has focused on closed teams in which the number of agents is fixed. In this work, we consider open teams by allowing agents with different fixed policies to enter and leave the environment without prior notification. Our solution builds on graph neural networks to learn agent models and joint-action value models under varying team compositions. We contribute a novel action-value computation that integrates the agent model and joint-action value model to produce action-value estimates. We empirically demonstrate that our approach successfully models the effects other agents have on the learner, leading to policies that robustly adapt to dynamic team compositions and significantly outperform several alternative methods.
@inproceedings{rahman2021open,
title={Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning},
author={Arrasy Rahman and Niklas H\"opner and Filippos Christianos and Stefano V. Albrecht},
booktitle={International Conference on Machine Learning (ICML)},
year={2021}
}
2020
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
Abstract:
Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with previously unknown teammates. Prior work in this area has focused on closed teams in which the number of agents is fixed. In this work, we consider open teams by allowing agents of varying types to enter and leave the team without prior notification. Our proposed solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives. We demonstrate empirically that our approach results in agent policies which can robustly adapt to dynamic team composition, and is able to effectively generalize to larger teams than were seen during training.
@misc{rahman2020open,
title={Open Ad Hoc Teamwork using Graph-based Policy Learning},
author={Arrasy Rahman and Niklas H\"opner and Filippos Christianos and Stefano V. Albrecht},
year={2020},
eprint={2006.10412},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
Local Information Opponent Modelling Using Variational Autoencoders
arXiv:2006.09447, 2020
Abstract | BibTex | arXiv
deep-rlagent-modelling
Abstract:
Modelling the behaviours of other agents (opponents) is essential for understanding how agents interact and making effective decisions. Existing methods for opponent modelling commonly assume knowledge of the local observations and chosen actions of the modelled opponents, which can significantly limit their applicability. We propose a new modelling technique based on variational autoencoders, which are trained to reconstruct the local actions and observations of the opponent based on embeddings which depend only on the local observations of the modelling agent (its observed world state, chosen actions, and received rewards). The embeddings are used to augment the modelling agent's decision policy which is trained via deep reinforcement learning; thus the policy does not require access to opponent observations. We provide a comprehensive evaluation and ablation study in diverse multi-agent tasks, showing that our method achieves comparable performance to an ideal baseline which has full access to opponent's information, and significantly higher returns than a baseline method which does not use the learned embeddings.
@misc{papoudakis2020opponent,
title={Local Information Opponent Modelling Using Variational Autoencoders},
author={Georgios Papoudakis and Filippos Christianos and Stefano V. Albrecht},
year={2020},
eprint={2006.09447},
archivePrefix={arXiv},
primaryClass={cs.LG}
}