Evaluating the Coordination of Agents in Multi-agent Reinforcement Learning

Author:

Barton Sean L.,Zaroukian Erin,Asher Derrik E.,Waytowich Nicholas R.

Publisher

Springer International Publishing

Reference7 articles.

1. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)

2. 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)

3. 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)

4. Sugihara, G., May, R., Ye, H., Hsieh, C.-h., Deyle, E., Fogarty, M., Munch, S.: Detecting causality in complex ecosystems. Science, 1227079 (2012)

5. 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

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