Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path

Liyu Chen, Mehdi Jafarnia-Jahromi, Rahul Jain, Haipeng Luo

Keywords: reinforcement learning, stochastic shortest path, regret minimization

Abstract: 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 approximates the SSP model by a finite-horizon counterpart only in the analysis without explicit implementation. Using this template, we develop two new algorithms: the first one is model-free (the first in the literature to our knowledge) and minimax optimal under strictly positive costs; the second one is model-based and minimax optimal even with zero-cost state-action pairs, matching the best existing result from [Tarbouriech et al., 2021b]. Importantly, both algorithms admit highly sparse updates, making them computationally more efficient than all existing algorithms. Moreover, both can be made completely parameter-free.

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TL;DR: Implicit Finite-Horizon Approximation of SSP