Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games

8 Jul 2019Eric MazumdarLillian J. RatliffMichael I. JordanS. Shankar Sastry

We show by counterexample that policy-gradient algorithms have no guarantees of even local convergence to Nash equilibria in continuous action and state space multi-agent settings. To do so, we analyze gradient-play in N-player general-sum linear quadratic games, a classic game setting which is recently emerging as a benchmark in the field of multi-agent learning... (read more)

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