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Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings., , , and . CoRR, (2019)Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in 'Almost All' Zero-Sum Continuous Games., and . CoRR, (2020)Feedback Linearization for Unknown Systems via Reinforcement Learning., , , , , , and . CoRR, (2019)Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization., , , , and . CoRR, (2021)Expert Selection in High-Dimensional Markov Decision Processes., , , , and . CDC, page 3604-3610. IEEE, (2020)Gradient-based inverse risk-sensitive reinforcement learning., , , and . CDC, page 5796-5801. IEEE, (2017)On the Convergence of Competitive, Multi-Agent Gradient-Based Learning., and . CoRR, (2018)Inverse Risk-Sensitive Reinforcement Learning., and . IEEE Trans. Automat. Contr., 65 (3): 1256-1263 (2020)Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings., , , and . CoRR, (2019)A Multi-Armed Bandit Approach for Online Expert Selection in Markov Decision Processes., , , , and . CoRR, (2017)