Efficient communication using reinforcement learning in a cooperative navigation game

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/304833
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Bibliographical item details
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Type: Examensarbete för masterexamen
Title: Efficient communication using reinforcement learning in a cooperative navigation game
Authors: Bohman, Erik
Rogmalm Hornestedt, Simon
Abstract: The thesis aims to investigate if agents are able to develop an efficient communication, with semantic meanings, and solve a navigation problem, using reinforcement learning. Additionally, it aims to evaluate the relevancy and benefit of one and two-way communication in comparison to each other and no communication. The problem is tackled in a multi-agent system (two agents), using a cooperative navigation game. The agents possess different privately held information, they are hence equipped with a communication channel and a language with no initial semantic meaning to convey the information to each other and solve the task of finding a target inside an environment with distracting obstacles. The experiments take place in both a discrete and a continuous setting with a varying number of communication ways and are evaluated based on the average time to complete the navigation. It is shown in the thesis that the agents can develop a language with a semantic meaning, which contributes to an efficient communication when set in a discrete environment and in a continuous static environment. However, it is inconclusive whether there are any significant benefits to be gained from a two-way communication compared to a one-way communication and whether the task can be solved in a continuous non-static environment.
Keywords: machine learning;reinforcement learning;efficient communication;multiagent system
Issue Date: 2022
Publisher: Chalmers tekniska högskola / Institutionen för data och informationsteknik
URI: https://hdl.handle.net/20.500.12380/304833
Collection:Examensarbeten för masterexamen // Master Theses



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