Efficient communication using reinforcement learning in a cooperative navigation game
| dc.contributor.author | Bohman, Erik | |
| dc.contributor.author | Rogmalm Hornestedt, Simon | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.examiner | Dubhashi, Devdatt | |
| dc.contributor.supervisor | Johansson, Moa | |
| dc.contributor.supervisor | Carlsson, Emil | |
| dc.date.accessioned | 2022-06-21T06:02:33Z | |
| dc.date.available | 2022-06-21T06:02:33Z | |
| dc.date.issued | 2022 | sv |
| dc.date.submitted | 2020 | |
| dc.description.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. | sv |
| dc.identifier.coursecode | DATX05 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/304833 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | machine learning | sv |
| dc.subject | reinforcement learning | sv |
| dc.subject | efficient communication | sv |
| dc.subject | multiagent system | sv |
| dc.title | Efficient communication using reinforcement learning in a cooperative navigation game | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H |