A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning

1 code implementation • 11 Oct 2022 • Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, Stefano V. Albrecht

These belief estimates are combined with our solution for the fully observable case to compute an agent's optimal policy under partial observability in open ad hoc teamwork.

Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods

1 code implementation • 28 Jan 2022 • Niklas Höpner, Ilaria Tiddi, Herke van Hoof

Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains.

Knowledge Graphs Policy Gradient Methods +2

Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning

1 code implementation • 18 Jun 2020 • Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht

Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with teammates without prior coordination mechanisms, including joint training.