GAIG Game AI Research Group @ QMUL

Action Advising with Advice Imitation in Deep Reinforcement Learning


Abstract

Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Recently proposed student-initiated approaches have obtained promising results. However, due to being in the early stages of development, these also have some substantial shortcomings. One of the abilities that are absent in the current methods is further utilising advice by reusing, which is especially crucial in the practical settings considering the budget constraints in peer-to-peer interactions. In this study, we present an approach to enable the student agent to imitate previously acquired advice to reuse them directly in its exploration policy, without any interventions in the learning mechanism itself. In particular, we employ a behavioural cloning module to imitate the teacher policy and use dropout regularisation to have a notion of epistemic uncertainty to keep track of which state-advice pairs are actually collected. As the results of experiments we conducted in three Atari games show, advice reusing via imitation is indeed a feasible option in deep RL and our approach can successfully achieve this while significantly improving the learning performance, even when it is paired with a simple early advising heuristic.

Cite this work

@inproceedings{ilhan2021action,
author= {Ilhan, Ercument and Gow, Jeremy and Perez-Liebana, Diego},
title= {{Action Advising with Advice Imitation in Deep Reinforcement Learning}},
year= {2021},
booktitle= {{Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems}},
abstract= {Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Recently proposed student-initiated approaches have obtained promising results. However, due to being in the early stages of development, these also have some substantial shortcomings. One of the abilities that are absent in the current methods is further utilising advice by reusing, which is especially crucial in the practical settings considering the budget constraints in peer-to-peer interactions. In this study, we present an approach to enable the student agent to imitate previously acquired advice to reuse them directly in its exploration policy, without any interventions in the learning mechanism itself. In particular, we employ a behavioural cloning module to imitate the teacher policy and use dropout regularisation to have a notion of epistemic uncertainty to keep track of which state-advice pairs are actually collected. As the results of experiments we conducted in three Atari games show, advice reusing via imitation is indeed a feasible option in deep RL and our approach can successfully achieve this while significantly improving the learning performance, even when it is paired with a simple early advising heuristic.},
}

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