Learning to Perceive and Act by Trial and Error

Whitehead, Steven D. ; Ballard, Dana H.
Springer
Published 1991
ISSN:
0885-6125
Keywords:
Reinforcement learning ; deictic representations ; sensory-motor integration ; hidden state ; non-Markov decision problems
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract This article considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phoenomenon perceptual aliasingand show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. We then describe a new decision system that overcomes these difficulties for a restricted class of decision problems. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its visual attention in order to collect necessary sensory information.
Type of Medium:
Electronic Resource
URL: