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Approximating the Value Function for Continuous Space Reinforcement Learning in Robot Control (bibtex)
by S Buck, M Beetz and T Schmitt
Abstract:
Many robot learning tasks are very difficult to solve: their state spaces are high dimensional, variables and command parameters are continuously valued, and system states are only partly observable. In this paper, we propose to learn a continuous space value function for reinforcement learning using neural networks trained from data of exploration runs. The learned function is guaranteed to be a lower bound for, and reproduces the characteristic shape of, the accurate value function. We apply our approach to two robot navigation tasks, discuss how to deal with possible problems occurring in practice, and assess its performance.
Reference:
Approximating the Value Function for Continuous Space Reinforcement Learning in Robot Control (S Buck, M Beetz and T Schmitt), In Proc. of the IEEE Intl. Conf. on Intelligent Robots and Systems, 2002.
Bibtex Entry:
@inproceedings{buck_approximating_2002,
author = {S Buck and M Beetz and T Schmitt},
title = {Approximating the Value Function for Continuous Space Reinforcement
Learning in Robot Control},
booktitle = {Proc. of the {IEEE} Intl. Conf. on Intelligent Robots and Systems},
year = {2002},
abstract = {Many robot learning tasks are very difficult to solve: their state
spaces are high dimensional, variables and command parameters are
continuously valued, and system states are only partly observable.
In this paper, we propose to learn a continuous space value function
for reinforcement learning using neural networks trained from data
of exploration runs. The learned function is guaranteed to be a lower
bound for, and reproduces the characteristic shape of, the accurate
value function. We apply our approach to two robot navigation tasks,
discuss how to deal with possible problems occurring in practice,
and assess its performance.},
}
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