no code implementations • 16 Apr 2024 • Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman, Peter Stone
POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors.
no code implementations • 18 Aug 2023 • Arrasy Rahman, Jiaxun Cui, Peter Stone
In this work, we first propose that maximizing an AHT agent's robustness requires it to emulate policies in the minimum coverage set (MCS), the set of best-response policies to any partner policies in the environment.
no code implementations • 9 Feb 2023 • Elliot Fosong, Arrasy Rahman, Ignacio Carlucho, Stefano V. Albrecht
Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-stationarity caused by mutually adapting agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
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.
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.
no code implementations • 28 Jul 2022 • Arrasy Rahman, Elliot Fosong, Ignacio Carlucho, Stefano V. Albrecht
Early approaches address the AHT challenge by training the learner with a diverse set of handcrafted teammate policies, usually designed based on an expert's domain knowledge about the policies the learner may encounter.
no code implementations • 19 Jul 2022 • Elliot Fosong, Arrasy Rahman, Ignacio Carlucho, Stefano V. Albrecht
We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and together must learn to adapt to an unseen but related task.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 16 Feb 2022 • Reuth Mirsky, Ignacio Carlucho, Arrasy Rahman, Elliot Fosong, William Macke, Mohan Sridharan, Peter Stone, Stefano V. Albrecht
Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination.
1 code implementation • 15 Feb 2021 • Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht
Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
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.
no code implementations • 11 Jun 2019 • Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains.