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Stefano-V.-Albrechttransfer-learning
2023
Elliot Fosong, Arrasy Rahman, Ignacio Carlucho, Stefano V. Albrecht
Learning Complex Teamwork Tasks Using a Sub-task Curriculum
AAMAS Workshop on Multiagent Sequential Decision Making Under Uncertainty, 2023
Abstract | BibTex | arXiv | Code
AAMASmulti-agent-rlad-hoc-teamworktransfer-learning
Abstract:
Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large policy space, and non-stationarity caused by mutually adapting agents. To facilitate efficient learning of complex multi-agent tasks, we propose an approach which uses an expert-provided curriculum of simpler multi-agent sub-tasks. In each sub-task of the curriculum, a subset of the entire team is trained to acquire sub-task-specific policies. The sub-teams are then merged and transferred to the target task, where their policies are collectively fined tuned to solve the more complex target task. We present MEDoE, a flexible method which identifies situations in the target task where each agent can use its sub-task-specific skills, and uses this information to modulate hyperparameters for learning and exploration during the fine-tuning process. We compare MEDoE to multi-agent reinforcement learning baselines that train from scratch in the full task, and with naïve applications of standard multi-agent reinforcement learning techniques for fine-tuning. We show that MEDoE outperforms baselines which train from scratch or use naïve fine-tuning approaches, requiring significantly fewer total training timesteps to solve a range of complex teamwork tasks.
@inproceedings{fosong2023learning,
title={Learning complex teamwork tasks using a sub-task curriculum},
author={Elliot Fosong, Arrasy Rahman, Ignacio Carlucho and Stefano V. Albrecht},
booktitle={AAMAS Workshop on Multiagent Sequential Decision Making under Uncertainty},
year={2023},
}
2022
Shangmin Guo, Yi Ren, Stefano V. Albrecht, Kenny Smith
Sample Relationships through the Lens of Learning Dynamics with Label Information
NeurIPS Workshop on Interpolation and Beyond, 2022
Abstract | BibTex | arXiv
NeurIPSiterated-learningdeep-learningtransfer-learning
Abstract:
Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the data, which is also an important factor for training ANNs. In this work, we start from approximating the interaction between two samples, i.e. how learning one sample would modify the model's prediction on the other sample. Through analysing the terms involved in weight updates in supervised learning, we find that the signs of labels influence the interactions between samples. Therefore, we propose the labelled pseudo Neural Tangent Kernel (lpNTK) which takes label information into consideration when measuring the interactions between samples. We first prove that lpNTK would asymptotically converge to the well-known empirical Neural Tangent Kernel in terms of the Frobenius norm under certain assumptions. Secondly, we illustrate how lpNTK helps to understand learning phenomena identified in previous work, specifically the learning difficulty of samples and forgetting events during learning. Moreover, we also show that lpNTK can help to improve the generalisation performance of ANNs in image classification tasks, compared with the original whole training sets.
@inproceedings{guo2022relationship,
title={Sample Relationships through the Lens of Learning Dynamics with Label Information},
author={Shangmin Guo and Yi Ren and Stefano V. Albrecht and Kenny Smith},
booktitle={NeurIPS 2022 Workshop on Interpolation and Beyond},
year={2022}
}