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On the stability of Lipschitz continuous control problems and its application to reinforcement learning
We address the crucial yet underexplored stability properties of the Hamilton--Jacobi--Bellman (HJB) equation in model-free reinforcement learning contexts, specifically for Lipschitz continuous optimal control problems.
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
For linear bandits, we achieve $\tilde O(\min\{d\sqrt{K}, d^{1. 5}\sqrt{\sum_{k=1}^K \sigma_k^2}\} + d^2)$ where $d$ is the dimension of the features, $K$ is the time horizon, and $\sigma_k^2$ is the noise variance at time step $k$, and $\tilde O$ ignores polylogarithmic dependence, which is a factor of $d^3$ improvement.
Training Wasserstein GANs without gradient penalties
We propose a stable method to train Wasserstein generative adversarial networks.
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