Search Results for author: Arrasy Rahman

Found 11 papers, 5 papers with code

N-Agent Ad Hoc Teamwork

no code implementations16 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.

Autonomous Driving Multi-agent Reinforcement Learning +4

Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents

no code implementations18 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.

Diversity

Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition

1 code implementation9 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

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

1 code implementation11 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.

Graph Neural Network

Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity

no code implementations28 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.

Diversity valid

Few-Shot Teamwork

no code implementations19 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

A Survey of Ad Hoc Teamwork Research

no code implementations16 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.

Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

1 code implementation15 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

Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning

1 code implementation18 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.

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

no code implementations11 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.

Decision Making Meta-Learning +3

Cannot find the paper you are looking for? You can Submit a new open access paper.