AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A novel framework is proposed that decouples operator estimation from value function learning and establishes the smoothness of $Q^*$ through the Bellman equation using the the Holder assumptions on the conditional reward and transition densities.
Offline maximum entropy reinforcement learning seeks to estimate the optimal soft action-value function \(Q^*\) from a fixed dataset without further interaction with the environment. Although \(Q^*\) is characterized by the soft optimal Bellman equation, the corresponding Bellman operator cannot be evaluated directly b...
Gaussian diffusion models offer a practical framework for generative modeling. Denoising L\'{e}vy Probabilistic Models (DLPM) introduce heavy-tailed perturbations with a fixed tail index throughout denoising. We introduce Annealed L\'{e}vy Diffusion (ALD). ALD varies the stability index $\alpha(t)$ to combine heavy-tai...
Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and the unlabeled target domain. We introduce Joint Contrastive-Adversarial Representation Learning (JCAR), a two-stage framework that learns a common representation from the...
In this work, we propose a transfer learning algorithm for covariate shift, integrating sample reweighting with representation learning. A key feature of our approach is the use of a lower-dimensional latent space for modeling, in contrast to conventional methods that operate directly in the high-dimensional ambient sp...
Finite-sample bounds are derived that separate the effects of critic estimation, neural drift regression, finite-sample SNIS, diffusion discretization, and inherited actor error on expected average policy suboptimality.
A weighted Bellman residual minimization framework that incorporates density ratio weighting by effectively integrating expert demonstrations with behavioral data is introduced, providing specific guidance for the rational utilization of expert demonstrations.
In offline RL, estimating the optimal action-value function $Q^*$ can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental challenge is that the reward function and transition kernel are unknown, so the optimal Bellman operator is not directly observable from data. T...
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