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DREAM: Deep Regret minimization with Advantage baselines and Model-free learning
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We introduce DREAM, a deep reinforcement learning algorithm that finds optimal strategies in imperfect-information games with multiple agents. Formally, DREAM converges to a Nash Equilibrium in two-player zero-sum games and to an extensive-form coarse correlated equilibrium in all other games. Our primary innovation is an effective algorithm that, in contrast to other regret-based deep learning algorithms, does not require access to a perfect simulator of the game to achieve good performance. We show that DREAM empirically achieves state-of-the-art performance among model-free algorithms in popular benchmark games, and is even competitive with algorithms that do use a perfect simulator.
Forward citations
Cited by 6 Pith papers
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NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria
Periodically re-centering the KL-regularizer on the current policy in self-play yields a policy-gradient algorithm that, in its exact form, provably converges to a Nash equilibrium.
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