Augmenting a neural agent with an Oracle

In this thesis, I approached deep reinforcement learning agents with the novel idea of augmenting the agent with another neural network called an "Oracle" to gain more insights about the game environment. The Oracle is trained through supervised learning and can be used in various ways with the agent such as a reward shaper or in a pipeline with the agent through which it can transform the original input state into a more enhanced input state with more information. Overall results were not positive as creating a good Oracle can be hard. Creating a better Oracle could possibly display promising results.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Miranda, Zachery A

Advisor dc:contributor.advisor

• Armando Solar-Lezama.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

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

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119732

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119732