Learning to teach and meta-learning for sample-efficient multiagent reinforcement learning

Learning optimal policies in the presence of non-stationary policies of other simultaneously learning agents is a major challenge in multiagent reinforcement learning (MARL). The difficulty is further complicated by other challenges, including the multiagent credit assignment, the high dimensionality of the problems, and the lack of convergence guarantees. As a result, many experiences are often required to learn effective multiagent policies. This thesis introduces two frameworks to reduce the sample complexity in MARL. The first framework presented in this thesis provides a method to reduce the sample complexity by exchanging knowledge between agents. In particular, recent work on agents that learn to teach other teammates has demonstrated that action advising accelerates team-wide learning.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Kim, Dong Ki(Aeronautics and astronautics scientist)Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Jonathan P. How.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/128312

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/128312