We further propose a federated natural actor critic (NAC) method for multi-task RL with function approximation and stochastic policy evaluation, and establish its finite-time sample complexity taking the errors of function approximation into account.

Many articles in the literature solve the CMDP with an unknown environment.

For out of distribution (OOD) inference, it is desired that the model can assign high epistemic uncertainty to the OOD regions compared to their ID counterparts. A.2 Policy Gradient based Reward Maximization for Segmentation Backbone This approach enables us to efficiently achieve the optimal solution for reward maximization. We present some examples of generated OOD examples in Figure 1(a). The results are presented in Figure 1(b)-(d). In Table 1, we present the results of our uncertainty estimation framework when applied to the Cityscapes dataset.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

We present an application of the connectedness of these superlevel sets to the derivation of minimax theorems for robust reinforcement learning.

Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.