Efficient communication using reinforcement learning in a cooperative navigation game
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Bohman, Erik
Rogmalm Hornestedt, Simon
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
machine learning , reinforcement learning , efficient communication , multiagent system