regret bound

Provably Efficient Causal Reinforcement Learning with Confounded Observational Data NeurIPS 2021 Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions ICML 2021 Provably efficient multi-task reinforcement learning with model transfer NeurIPS 2021 Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping ICML 2021 Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints NeurIPS 2021 Provably Efficient Safe Exploration via Primal-Dual Policy Optimization AISTATS 2021 Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems L4DC 2021 Q-learning with Logarithmic Regret AISTATS 2021 Quantifying Differences in Reward Functions ICLR 2021 Randomized Exploration in Reinforcement Learning with General Value Function Approximation ICML 2021 Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits NeurIPS 2021 Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraints ICML 2021 Regret Bounds for Adaptive Nonlinear Control L4DC 2021 Regret Bounds for Batched Bandits AAAI 2021 Regret Bounds for Gaussian-Process Optimization in Large Domains NeurIPS 2021 Regret Bounds for Online Kernel Selection in Continuous Kernel Space AAAI 2021 Regulating Greed Over Time in Multi-Armed Bandits JMLR 2021 Reinforcement Learning in Parametric MDPs with Exponential Families AISTATS 2021 Reinforcement Learning with Trajectory Feedback AAAI 2021 Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits AAAI 2021 Reward Biased Maximum Likelihood Estimation for Reinforcement Learning L4DC 2021 Risk-averse Heteroscedastic Bayesian Optimization NeurIPS 2021 Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach ICML 2021 RL for Latent MDPs: Regret Guarantees and a Lower Bound NeurIPS 2021 Robust Bandit Learning with Imperfect Context AAAI 2021