Efficiently Solving MDPs with Stochastic Mirror Descent

Jul 12, 2020

Speakers

About

In this paper we present a unified framework based on primal-dual stochastic mirror descent for approximately solving infinite-horizon Markov decision processes (MDPs) given a generative model. When applied to an average-reward MDP with total actions and mixing time bound our method computes an -optimal policy with an expected (^2 ^-2) samples from the state-transition matrix, removing the ergodicity dependence of prior art. When applied to a γ-discounted MDP with A total actions our method computes an eps-optimal policy with an expected ((1-γ)^-4 ^-2) samples, improving over the best-known primal-dual methods while matching the state-of-the-art up to a (1-γ)^-1 factor. Both methods are model-free, update state values and policy simultaneously, and run in time linear in the number of samples taken.

Organizer

Categories

About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

Store presentation

Should this presentation be stored for 1000 years?

How do we store presentations

Total of 0 viewers voted for saving the presentation to eternal vault which is 0.0%

Sharing

Recommended Videos

Presentations on similar topic, category or speaker