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 • 9 Oct 2023 • Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Amos Storkey
Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or OtO) is a paradigm well matched to a real-world RL deployment process.
3 code implementations • 2 Aug 2022 • Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning.
1 code implementation • 12 Jul 2022 • Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah P. Hanna, Stefano V. Albrecht
Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training.
1 code implementation • 22 Jun 2022 • Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined.
2 code implementations • 11 Oct 2021 • Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens.