Library Open Repository
Concurrent Q-Learning for Autonomous Mapping and Navigation
Ollington, R and Vamplew, P (2003) Concurrent Q-Learning for Autonomous Mapping and Navigation. In: The 2nd International Conference on Computational Intelligence, Robotics and Autonomous Systems, 15-18 December 2003, Singapore.
CQL_Ollington_2...pdf | Download (245kB)
Available under University of Tasmania Standard License.
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Keywords:||goal-independent learning, reinforcement learning|
|Date Deposited:||01 Aug 2004|
|Last Modified:||18 Nov 2014 03:10|
|Item Statistics:||View statistics for this item|
Actions (login required)
|Item Control Page|