Abstract
While task generalisation is widely studied in the context of single-agent reinforcement learning (RL), little research exists in the context of multi-agent RL. The research that does exist usually considers task generalisation implicitly as a part of the environment, and when it is considered explicitly there are no theoretical guarantees. We propose Goal-Oriented Learning for Multi-Task Multi-Agent RL (GOLeMM), a method that achieves provably optimal task generalisation that, to the best of our knowledge, has not been achieved before in MARL. After learning an optimal goal-oriented value function for a single arbitrary task, our method can zero-shot infer the optimal policy for any other task in the distribution given only knowledge of the terminal rewards for each agent for the new task and learnt task. Empirically we show that our method is able to generalise over a full task distribution, while representative baselines are only able to learn a small subset of the task distribution.
Simon Rosen
I am an experienced software developer and a master’s candidate. My research is focused on sample efficiency in cooperative multiagent reinforcement learning by leveraging knowledge transfer.
Abdel Mfougouon Njupoun
I am an enthusiastic researcher with a passion for Mathematics, Robotics, and Reinforcement Learning. My work focuses on developing advanced algorithms and models that leverage the power of these disciplines to create intelligent and autonomous systems capable of making informed decisions and learning from their environment.
Geraud Nangue Tasse
Associate Lecturer
I am an IBM PhD fellow interested in reinforcement learning (RL) since it is the subfield of machine learning with the most potential for achieving AGI.
Benjamin Rosman
Lab Director
I am a Professor in the School of Computer Science and Applied Mathematics at the University of the Witwatersrand in Johannesburg. I work in robotics, artificial intelligence, decision theory and machine learning.