no code implementations • 22 Apr 2024 • Dongge Han, Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Peter Bell, Amos Storkey
We introduce LLM-Personalize, a novel framework with an optimization pipeline designed to personalize LLM planners for household robotics.
no code implementations • 27 Dec 2021 • Dongge Han, Chris Xiaoxuan Lu, Tomasz Michalak, Michael Wooldridge
By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control.
no code implementations • 18 Oct 2021 • Dongge Han, Sebastian Tschiatschek
Abstraction plays an important role in the generalisation of knowledge and skills and is key to sample efficient learning.
no code implementations • 25 Jun 2020 • Dongge Han, Michael Wooldridge, Alex Rogers, Olga Ohrimenko, Sebastian Tschiatschek
In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication.
no code implementations • 21 Oct 2019 • Dongge Han, Wendelin Boehmer, Michael Wooldridge, Alex Rogers
We evaluate our model empirically on a set of multi-agent pursuit and taxi tasks, and show that our agents learn to adapt flexibly across scenarios that require different termination behaviours.
Hierarchical Reinforcement Learning reinforcement-learning +1