Transfer of knowledge for a climbing Virtual Human: A reinforcement learning approach

B. Libeau, A. Micaelli, O. Sigaud
2009 2009 IEEE International Conference on Robotics and Automation  
In the reinforcement learning literature, transfer is the capability to reuse on a new problem what has been learnt from previous experiences on similar problems. Adapting transfer properties for robotics is a useful challenge because it can reduce the time spent in the first exploration phase on a new problem. In this paper we present a transfer framework adapted to the case of a climbing Virtual Human (VH). We show that our VH learns faster to climb a wall after having learnt on a different previous wall.
doi:10.1109/robot.2009.5152553 dblp:conf/icra/LibeauMS09 fatcat:d6wevn3sa5fy7g3fqsccm5bd2u