Learning against sequential opponents in repeated stochastic games

20 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning with Opponent-Learning Awareness (2017, background): “Furthermore, Hernandez-Leal and Kaisers [18] directly model the distribution over opponents.”
A survey and critique of multiagent deep reinforcement learning (2018, background): “online learned opponent model [139,144] to construct a rectified belief over the opponent strategy.”
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity (2017, background): “These ideas (e.g., reusing past policies) have inspired recent works on multiagent systems (Hernandez-Leal et al., 2016a; Hernandez-Leal and Kaisers, 2017b).”