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surveydeep-rlmulti-agent-rlagent-modellingad-hoc-teamworkautonomous-drivinggoal-recognitionexplainable-aicausalgeneralisationsecurityemergent-communicationiterated-learningintrinsic-rewardsimulatorstate-estimationdeep-learningtransfer-learning
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Arrasy-RahmanFilippos-Christianos
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
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}
}
Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
International Conference on Machine Learning, 2021
Abstract | BibTex | arXiv | Video | Code
ICMLdeep-rlmulti-agent-rl
Abstract:
Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents. Parameter sharing between agents significantly decreases the number of trainable parameters, shortening training times to tractable levels, and has been linked to more efficient learning. However, having all agents share the same parameters can also have a detrimental effect on learning. We demonstrate the impact of parameter sharing methods on training speed and converged returns, establishing that when applied indiscriminately, their effectiveness is highly dependent on the environment. We propose a novel method to automatically identify agents which may benefit from sharing parameters by partitioning them based on their abilities and goals. Our approach combines the increased sample efficiency of parameter sharing with the representational capacity of multiple independent networks to reduce training time and increase final returns.
@inproceedings{christianos2021scaling,
title={Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing},
author={Filippos Christianos and Georgios Papoudakis and Arrasy Rahman 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}
}
2019
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
Abstract:
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their actions. In such multi-agent environments, additional learning problems arise due to the continually changing decision-making policies of agents. This paper surveys recent works that address the non-stationarity problem in multi-agent deep reinforcement learning. The surveyed methods range from modifications in the training procedure, such as centralized training, to learning representations of the opponent's policy, meta-learning, communication, and decentralized learning. The survey concludes with a list of open problems and possible lines of future research.
@misc{papoudakis2019dealing,
title={Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning},
author={Georgios Papoudakis and Filippos Christianos and Arrasy Rahman and Stefano V. Albrecht},
year={2019},
eprint={1906.04737},
archivePrefix={arXiv},
primaryClass={cs.LG}
}