Title:
A Multistrategy Case-Based and Reinforcement Learning Approach to Self-Improving Reactive Control Systems for Autonomous Robotic Navigation
A Multistrategy Case-Based and Reinforcement Learning Approach to Self-Improving Reactive Control Systems for Autonomous Robotic Navigation
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Author(s)
Ram, Ashwin
Santamaria, Juan Carlos
Santamaria, Juan Carlos
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Abstract
This paper presents a self-improving reactive
control system for autonomous robotic navigation.
The navigation module uses a schema-based
reactive control system to perform the
navigation task. The learning module combines
case-based reasoning and reinforcement learning
to continuously tune the navigation system
through experience. The case-based reasoning
component perceives and characterizes the
system’s environment, retrieves an appropriate
case, and uses the recommendations of the case
to tune the parameters of the reactive control
system. The reinforcement learning component
refines the content of the cases based on the current
experience. Together, the learning components
perform on-line adaptation, resulting in
improved performance as the reactive control
system tunes itself to the environment, as well as
on-line learning, resulting in an improved library
of cases that capture environmental regularities
necessary to perform on-line adaptation. The
system is extensively evaluated through simulation
studies using several performance metrics
and system configurations.
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Date Issued
1993
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Text
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Paper