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Creators/Authors contains: "Foster, D"

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  1. ; ; ; ; (, Proceedings of the Eleventh International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  2. ; ; (, Proceedings of the International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, arXiv preprint)
    Accepted Manuscript Full Text Available
  4. ; ; (, 34th Annual Conference on Neural Information Processing Systems (NeurIPS), NeurIPS 2020)
    null (Ed.)
    We obtain global, non-asymptotic convergence guarantees for independent learning algorithms in competitive reinforcement learning settings with two agents (i.e., zero-sum stochastic games). We consider an episodic setting where in each episode, each player independently selects a policy and observes only their own actions and rewards, along with the state. We show that if both players run policy gradient methods in tandem, their policies will converge to a min-max equilibrium of the game, as long as their learning rates follow a two-timescale rule (which is necessary). To the best of our knowledge, this constitutes the first finite-sample convergence result for independent policy gradient methods in competitive RL; prior work has largely focused on centralized, coordinated procedures for equilibrium computation. 
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    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; ; ; (, 2020 6th IEEE International Conference on Cloud and Big Data Computing (CBDCOM 2020))
    null (Ed.)
    Full Text Available