English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Planning problems are hard, motion planning, for example, isPSPACE-hard. Suchproblems are even more difficult in the presence of uncertainty. Although,Markov Decision Processes (MDPs) provide a formal framework for such problems,finding solutions to high dimensional continuous MDPs is usually difficult,especially when the actions and time measurements are continuous. Fortunately,problem-specific knowledge allows us to design controllers that are goodlocally, though having no global guarantees. We propose a method ofnonparametrically combining local controllers to obtain globally goodsolutions. We apply this formulation to two types of problems : motion planning(stochastic shortest path) and discounted MDPs. For motion planning, we arguethat usual MDP optimality criterion (expected cost) may not be practicallyrelevant. Wepropose an alternative: finding the minimum cost path,subject tothe constraint that the robot must reach the goal withhigh probability. Forthis problem, we prove that a polynomial number of samples is sufficient toobtain a high probability path. For discounted MDPs, we propose a formulationthat explicitly deals with model uncertainty, i.e., the problem introduced whentransition probabilities are not known exactly. We formulate the problem as arobust linear program which directly incorporates this type of uncertainty.

Robust Combination of Local Controllers