no code implementations • 4 Dec 2023 • Lukas Schäfer, Logan Jones, Anssi Kanervisto, Yuhan Cao, Tabish Rashid, Raluca Georgescu, Dave Bignell, Siddhartha Sen, Andrea Treviño Gavito, Sam Devlin
Video games have served as useful benchmarks for the decision making community, but going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community.
no code implementations • 18 Apr 2023 • Alain Andres, Lukas Schäfer, Esther Villar-Rodriguez, Stefano V. Albrecht, Javier Del Ser
Motivated by the recent success of Offline RL and Imitation Learning (IL), we conduct a study to investigate whether agents can leverage offline data in the form of trajectories to improve the sample-efficiency in procedurally generated environments.
no code implementations • 7 Feb 2023 • Lukas Schäfer, Oliver Slumbers, Stephen Mcaleer, Yali Du, Stefano V. Albrecht, David Mguni
In this work, we propose ensemble value functions for multi-agent exploration (EMAX), a general framework to seamlessly extend value-based MARL algorithms with ensembles of value functions.
no code implementations • 22 Dec 2022 • Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas, Georgios Papoudakis, Lukas Schäfer, Andrew Wing Keung To, Kuan-Ho Lao, Murat Cubuktepe, Matthew Haley, Peter Börsting, Stefano V. Albrecht
We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse.
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 • 5 Jul 2022 • Lukas Schäfer, Filippos Christianos, Amos Storkey, Stefano V. Albrecht
We show that a team of agents is able to adapt to novel tasks when provided with task embeddings.
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.
no code implementations • 13 May 2022 • Hanna Krasowski, Jakob Thumm, Marlon Müller, Lukas Schäfer, Xiao Wang, Matthias Althoff
We categorize the methods based on how they adapt the action: action replacement, action projection, and action masking.
1 code implementation • 29 Nov 2021 • Rujie Zhong, Duohan Zhang, Lukas Schäfer, Stefano V. Albrecht, Josiah P. Hanna
Reinforcement learning (RL) algorithms are often categorized as either on-policy or off-policy depending on whether they use data from a target policy of interest or from a different behavior policy.
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.
1 code implementation • ICML Workshop URL 2021 • Lukas Schäfer, Filippos Christianos, Josiah P. Hanna, Stefano V. Albrecht
Intrinsic rewards can improve exploration in reinforcement learning, but the exploration process may suffer from instability caused by non-stationary reward shaping and strong dependency on hyperparameters.
8 code implementations • 14 Jun 2020 • Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult.
3 code implementations • NeurIPS 2020 • Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards.