Learning factored transition models of structured environments has been shown to provide significant leverage when computing optimal policies for tasks within those environments. Previous work has focused on learning the structure of factored Markov Decision Processes (MDPs) with finite sets of states and actions. In this work we present an algorithm for online incremental learning of transitio...

The aim of this note is a classification of all nice and all inductively factored reflection arrangements. It turns out that apart from the supersolvable instances only the monomial groups G(r, r, 3) for r > 3 give rise to nice reflection arrangements. As a consequence of this and of the classification of all inductively free reflection arrangements from Hoge and Röhrle (2015) we deduce that th...

In domain-independent planning, dependencies of operators and variables often prevent the effective application of planning techniques that rely on “loosely coupled” problems (like factored planning or partial order reduction). In this paper, we propose a generic approach for factorizing a classical planning problem into an equivalent problem with fewer operator and variable dependencies. Our a...

We present the Factored Policy Gradient (FPG) planner: a probabilistic temporal planner designed to scale to large planning domains by applying two significant approximations. Firstly, we use a “direct” policy search in the sense that we attempt to directly optimise a parameterised plan using gradient ascent. Secondly, the policy is factored into a per action mapping from a partial observation ...

A complex algebra A is called ideally factored if Ia = Ca is a left ideal of A for all a ∈ A. In this article, we investigate some interesting properties of ideally factored algebras and show that these algebras are always Arens regular but never amenable. In addition, we investigate ρhomomorphisms and (ρ, τ)-derivations on ideally factored algebra.