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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 30 / No. 1: Thirtieth AAAI Conference On Artificial Intelligence

Incremental Stochastic Factorization for Online Reinforcement Learning

March 8, 2023

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Authors

Andre Barreto

Laboratório Nacional de Computação Científica


Rafael Beirigo

Laboratório Nacional de Computação Científica


Joelle Pineau

McGill University


Doina Precup

McGill University


DOI:

10.1609/aaai.v30i1.10311


Abstract:

A construct that has been receiving attention recently in reinforcement learning is stochastic factorization (SF), a particular case of non-negative factorization (NMF) in which the matrices involved are stochastic. The idea is to use SF to approximate the transition matrices of a Markov decision process (MDP). This is useful for two reasons. First, learning the factors of the SF instead of the transition matrices can reduce significantly the number of parameters to be estimated. Second, it has been shown that SF can be used to reduce the number of operations needed to compute an MDP's value function. Recently, an algorithm called expectation-maximization SF (EMSF) has been proposed to compute a SF directly from transitions sampled from an MDP. In this paper we take a closer look at EMSF. First, by exploiting the assumptions underlying the algorithm, we show that it is possible to reduce it to simple multiplicative update rules similar to the ones that helped popularize NMF. Second, we analyze the optimization process underlying EMSF and find that it minimizes a modified version of the Kullback-Leibler divergence that is particularly well-suited for learning a SF from data sampled from an arbitrary distribution. Third, we build on this improved understanding of EMSF to draw an interesting connection with NMF and probabilistic latent semantic analysis. We also exploit the simplified update rules to introduce a new version of EMSF that generalizes and significantly improves its precursor. This new algorithm provides a practical mechanism to control the trade-off between memory usage and computing time, essentially freeing the space complexity of EMSF from its dependency on the number of sample transitions. The algorithm can also compute its approximation incrementally, which makes it possible to use it concomitantly with the collection of data. This feature makes the new version of EMSF particularly suitable for online reinforcement learning. Empirical results support the utility of the proposed algorithm.

Topics: AAAI

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HOW TO CITE:

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup Incremental Stochastic Factorization for Online Reinforcement Learning Proceedings of the AAAI Conference on Artificial Intelligence, 30 (2016) .

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup Incremental Stochastic Factorization for Online Reinforcement Learning AAAI 2016, .

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup (2016). Incremental Stochastic Factorization for Online Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 30, .

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup. Incremental Stochastic Factorization for Online Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 30 2016 p..

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup. 2016. Incremental Stochastic Factorization for Online Reinforcement Learning. "Proceedings of the AAAI Conference on Artificial Intelligence, 30". .

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup. (2016) "Incremental Stochastic Factorization for Online Reinforcement Learning", Proceedings of the AAAI Conference on Artificial Intelligence, 30, p.

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup, "Incremental Stochastic Factorization for Online Reinforcement Learning", AAAI, p., 2016.

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup. "Incremental Stochastic Factorization for Online Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 30, 2016, p..

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup. "Incremental Stochastic Factorization for Online Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 30, (2016): .

Andre Barreto|| Rafael Beirigo|| Joelle Pineau|| Doina Precup. Incremental Stochastic Factorization for Online Reinforcement Learning. AAAI[Internet]. 2016[cited 2023]; .


ISSN: 2374-3468


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