The focus is on decentralized temporal-difference (TD) learning with linear function approximation in the presence of unreliable or even malicious agents, termed as Byzantine agents. In order to ...

@Article{Tadic:2001, author = "Tadi\'{c}, Vladislav", title = "On the Convergence of Temporal-Difference Learning with Linear Function Approximation", journal ...

Chen, Z., Zhang, S., Doan, T. T., Clarke, J. P., & Maguluri, S. T. (2019). Finite-sample analysis of nonlinear stochastic approximation with applications in ...

@InProceedings{Sutton+MPBSSW:2009, author = "Sutton, Richard S. and Maei, Hamid Reza and Precup, Doina and Bhatnagar, Shalabh and Silver, David and Szepesv{\'a}ri, Csaba and Wiewiora, Eric", title = ...

is one of the most popular forms of TD learning for linear function approximation. The reason is that multi-step methods often yield substantially better performance than their single-step ...

We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we ...