Abstract
The present study provides an in-depth analysis of inter-agent coordination through a complete exploration of agent behavioral dimensions. We evaluate the behavioral dimensions in a multi-agent predator-prey pursuit task where predator agent coordination necessarily exists due to a shared goal. We explore two conditions, one that is void of explicit coordination (fixed-strategy), and one that has the potential for explicit coordination (learning agents). This comprehensive evaluation of multi-agent behavioral dimensions provides theoretical evidence for true inter-agent coordination by a learning algorithm and the behavioral dimensions that agents coordinate in a cooperative task.
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- 1.
A prey agent was capable of 33% greater acceleration and 25% greater maximum velocity than the predator agents.
- 2.
In this case, training only impacted prey behavior, as fixed-strategy predators did not change their pursuit behavior through the learning process.
References
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Matignon, L., Laurent, G.J., Le Fort-Piat, N.: Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems. Knowl. Eng. Rev. 27, 1–31 (2012)
Lowe, R., Wu, Y., Tamar, A., Harb, J., Pieter Abbeel, O., Mordatch, I.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30, pp. 6382–6393. Curran Associates, Inc. (2017)
Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., Munch, S.: Detecting causality in complex ecosystems. Science, 1227079 (2012)
Barton, S.L., Waytowich, N.R., Zaroukian, E., Asher, D.E.: Measuring collaborative emergent behavior in multi-agent reinforcement learning. In: 1st International Conference on Human Systems Engineering and Design. IHSED; Springer
Brockman, G., et al.: OpenAI Gym. arXiv:1606.01540 [cs] (2016)
Clark, A.T., et al.: Spatial convergent cross mapping to detect causal relationships from short time series. Ecology. 96, 1174–1181 (2015)
Acknowledgements
This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-18-2-0058. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
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Barton, S.L., Zaroukian, E., Asher, D.E., Waytowich, N.R. (2019). Evaluating the Coordination of Agents in Multi-agent Reinforcement Learning. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_116
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DOI: https://doi.org/10.1007/978-3-030-11051-2_116
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