Undirected Networks
Deep Learning for Computing Convergence Rates of Markov Chains
Markovian models, estimating the rate of convergence to equilibrium is critical.
Policy Gradient for Rectangular Robust Markov Decision Processes Anonymous Author(s) Affiliation Address email
We provide a closed-form expression for the worst occupation measure.
Policy Gradient for Rectangular Robust Markov Decision Processes
However, they do not account for transition uncertainty, whereas learning robust policies can be computationally expensive.
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
Achieving O (1 /ε) Sample Complexity for Constrained Markov Decision Process
We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making.