3 accepted papers
We consider the problem of online reinforcement learning for the Stochastic Shortest Path (SSP) problem modeled as an unknown MDP with an absorbing state. We propose PSRL-SSP, a simple posterior sampling-based reinforcement learning algorithm for the SSP problem. The algorithm operates in epochs. At…
We introduce a generic template for developing regret minimization algorithms in the Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as certain properties are ensured. The key of our analysis is a new technique called implicit finite-horizon approximation, which a…
In this paper, we propose an approximate relative value learning (ARVL) algorithm for non- parametric MDPs with continuous state space and finite actions and average reward criterion. It is a sampling based algorithm combined with kernel density estimation and function approximation via nearest neig…