CQL_Ollington_2003.pdf (239.43 kB)
Concurrent Q-Learning for Autonomous Mapping and Navigation
conference contributionposted on 2023-05-26, 09:42 authored by Ollington, R, Vamplew, P
This paper presents a new algorithm for goal-independent Q-learning. The model was tested on a simulation of the Morris watermaze task. The new model learns faster than conventional Q-learning and experiences no interference when the goal location is moved. Once the new location has been discovered the system is able to navigate directly to the platform on subsequent trials. The model was also tested on watermaze tasks involving barriers. The presence of barriers did not affect the acquisition of "one-trial" learning. While presented as a navigational and mapping technique, the model could be applied to any reinforcement learning task with a variable reward structure.
Department/SchoolSchool of Computing
Event titleThe 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems
Date of Event (Start Date)2003-12-15
Date of Event (End Date)2003-12-18