5 papers
Reduce, Reuse, Recycle: Categories for Compositional Reinforcement Learning
Georgios Bakirtzis, Michail Savvas, Ruihan Zhao +2
In reinforcement learning, conducting task composition by forming cohesive, executable sequences from multiple tasks remains challenging. However, the ability to (de)compose tasks…
Cosection Localization for D-Manifolds and -Shifted Symplectic Derived Schemes, Revisited
Michail Savvas
This is a continuation of prior work of the author on cosection localization for d-manifolds. We construct reduced virtual fundamental classes for derived manifolds with surjective…
Good Moduli Spaces in Derived Algebraic Geometry
Eric Ahlqvist, Jeroen Hekking, Michele Pernice +1
We develop a theory of good moduli spaces for derived Artin stacks, which naturally generalizes the classical theory of good moduli spaces introduced by Alper. As such, many of the…
Localizing Virtual Structure Sheaves for Almost Perfect Obstruction Theories
Young-Hoon Kiem, Michail Savvas
Almost perfect obstruction theories were introduced in an earlier paper by the authors as the appropriate notion in order to define virtual structure sheaves and -theoretic inva…
K-Theoretic Generalized Donaldson-Thomas Invariants
Young-Hoon Kiem, Michail Savvas
We introduce the notion of almost perfect obstruction theory on a Deligne-Mumford stack and show that stacks with almost perfect obstruction theories have virtual structure sheaves…