Policy search approaches to reinforcement learning for quadruped locomotion

Legged locomotion is a challenging problem for machine learning to solve. A quadruped has 12 degrees of freedom which results in a large state space for the resulting Markov Decision Problem (DP). It is too difficult for computers to completely learn the state space, while it is too difficult for humans to fully understand the system dynamics and directly program the most efficient controller. This thesis combines these two approaches by integrating a model-based controller approach with reinforcement learning to develop an effective walk for a quadruped robot. We then evaluate different policy search approaches to reinforcement learning. To solve the Partially Observable Markov Decision Problem (POMIDP), a deterministic simulation is developed that generates a model which allows us to conduct a direct. policy search using dynamic programming. This is compared against using a, nondeterministic simulation to generate a model that evaluates policies. We show that using deterministic transitions to allow the use of dynamic programming has little impact on the performance of our system. Two local policy search approaches are implemented.

Department dc:contributor.department

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

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Jimenez, Antonio R

Advisor dc:contributor.advisor

• Leslie P. Kaelbling and Paul A. DeBitetto.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about 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/1721.1/36798

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/36798