Metric state space reinforcement learning for a vision-capable mobile robot
We address the problem of autonomously learning controllers for vision-capable mobile robots. We extend McCallum's (1995) Nearest-Sequence Memory algorithm to allow for general metrics over state-action trajectories. We demonstrate the feasibility of our
|Collections||ANU Research Publications|
|01_Zhumatiy_Metric_state_space_2006.pdf||646.07 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.