Learning environment simulators from sparse signals

To allow planning in novel environments that have not been mapped out by hand, we need ways of learning environment models. While conventional work has focused on video prediction as a means for environment learning, this work instead seeks to learn from much sparser signals, like the agent's reward. In Chapter 1, we establish a taxonomy of environments and the attributes that make them easier or harder to model through learning. In Chapter 2, we review prior work in the field of environment learning. In Chapter 3, we propose a model-learning architecture based purely on reward prediction, and analyze its performance on illustrative problems. Finally, in Chapter 4, we propose and evaluate a model-learning architecture that uses both reward and sparse "features" extracted from the environment.

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

2017

Author dc:creator

• Shavit, Yonadav Goldwasser

Advisor dc:contributor.advisor

• Leslie P. Kaelbling.

• 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/1721.1/113442

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/113442