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Provably Efficient Safe Exploration via Primal-Dual Policy Optimization., , , , and . CoRR, (2020)Provably Efficient Exploration in Policy Optimization., , , and . CoRR, (2019)Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations., , , , and . CoRR, (2020)Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach., , , , , , and . CoRR, (2021)Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy., , , and . CoRR, (2020)Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning., , , , , , and . ICLR, OpenReview.net, (2022)Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems., , , and . L4DC, volume 144 of Proceedings of Machine Learning Research, page 597-598. PMLR, (2021)Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret., , , , and . NeurIPS, (2020)Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy., , , and . NeurIPS, page 10564-10575. (2019)Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time., , and . NIPS, page 3383-3391. (2014)