A survey and critique of multiagent deep reinforcement learning

Author:

Hernandez-Leal PabloORCID,Kartal Bilal,Taylor Matthew E.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference368 articles.

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3. Agogino, A. K., & Tumer, K. (2008). Analyzing and visualizing multiagent rewards in dynamic and stochastic domains. Autonomous Agents and Multi-Agent Systems, 17(2), 320–338.

4. Ahamed, T. I., Borkar, V. S., & Juneja, S. (2006). Adaptive importance sampling technique for markov chains using stochastic approximation. Operations Research, 54(3), 489–504.

5. Albrecht, S. V., & Ramamoorthy, S. (2013). A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems. In Proceedings of the 12th international conference on autonomous agents and multi-agent systems. Saint Paul, MN, USA.

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