Learning RoboCup-Keepaway with Kernels

author: Tobias Jung, Department of Computer Science, University of Texas at Austin
published: Feb. 25, 2007,   recorded: June 2006,   views: 4695

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


We give another success story of using kernel-based methods to solve a dificult reinforcement learning problem, namely that of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in keepaway are the high-dimensionality of the state space (rendering conventional grid-based function approximation like tilecoding infeasable) and the stochasticity due to noise and multiple learning agents needing to co- operate. We use approximate policy iteration with sparsified regular- ization networks to carry out policy evaluation. Preliminary results indicate that the behavior learned through our approach clearly out- performs the best results obtained with tilecoding by Stone et al.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: