Advances in reinforcement learning research have recently produced agents that are competent, or sometimes exceed human performance, in complex tasks. Most interesting real world problems however, are not restricted to one agent, but instead deal with multiple agents acting in the same environment and have proven to be challenging tasks to solve. In this work we present a study on a homogeneous open population of agents modelled as a multi-agent reinforcement learning (MARL) system. Using the SimuLane highway traffic simulator as a test-bed we show experimentally that using a single-agent learnt policy to initialise the multi-agent scenario, which we then fine-tune to the task, out-performs agents that learn in the multi-agent setting from scratch. Specifically we contribute an open population MARL configuration, how to transfer knowledge from single- to a multi-agent setting and a training procedure for a homogeneous open population of agents.

Original languageEnglish
Title of host publicationProceedings of the 30th Benelux Conference on Artificial Intelligence
PublisherBenelux Association for Artificial Intelligence (BNVKI-AIABN)
Pages177-191
Publication statusPublished - 8 Nov 2018
Event30th Benelux Conference on Artificial Intelligence - BNAIC 2018 - Jheronimus Academy of Data Science, ‘s-Hertogenbosch, Netherlands
Duration: 8 Nov 20189 Nov 2018
https://bnaic2018.nl

Conference

Conference30th Benelux Conference on Artificial Intelligence - BNAIC 2018
CountryNetherlands
City‘s-Hertogenbosch
Period8/11/189/11/18
Internet address

ID: 39986932