Full experimental details are provided in Appendix C.

We study the problem of online adaptive policy selection for nonlinear time-varying discrete-time dynamical systems.

In this work, we conduct a comprehensive study of NetHack and examine various learning mechanisms to enhance the performance of neural models.

In this work, we introduce a framework for policy optimization based on mirror descent that naturally accommodates general parameterizations. The policy class induced by our scheme recovers known classes, e.g., softmax, and generates new ones depending on the choice of mirror map.

RL that utilize function approximation to generalize observational data to unknown states/actions. The goal of this paper is to study the sample complexity of policy-based RL, which is arguably the simplest setting for RL with function approximation (Kearns et al., 1999; Kakade, 2003).