Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization

• URL: http://arxiv.org/abs/2210.12057v1
• Date: Fri, 21 Oct 2022 15:49:20 GMT
• Title: Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization
• Authors: Gergely Neu, Nneka Okolo
• Abstract summary: We show that our method outputs a near-optimal policy after a number of queries to the generative model. Our method is computationally efficient and comes with the major advantage that it outputs a single softmax policy that is compactly represented by a low-dimensional parameter vector.
• Score: 12.411844611718958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new stochastic primal-dual optimization algorithm for planning in a large discounted Markov decision process with a generative model and linear function approximation. Assuming that the feature map approximately satisfies standard realizability and Bellman-closedness conditions and also that the feature vectors of all state-action pairs are representable as convex combinations of a small core set of state-action pairs, we show that our method outputs a near-optimal policy after a polynomial number of queries to the generative model. Our method is computationally efficient and comes with the major advantage that it outputs a single softmax policy that is compactly represented by a low-dimensional parameter vector, and does not need to execute computationally expensive local planning subroutines in runtime.

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