**Reference:**

L. Busoniu,
D. Ernst,
B. De Schutter, and
R. Babuska,
"Online least-squares policy iteration for reinforcement learning
control," *Proceedings of the 2010 American Control
Conference*, Baltimore, Maryland, pp. 486-491, June-July 2010.

**Abstract:**

Reinforcement learning is a promising paradigm for learning optimal
control. We consider policy iteration (PI) algorithms for
reinforcement learning, which iteratively *evaluate* and
*improve* control policies. State-of-the-art, least-squares
techniques for policy evaluation are sample-efficient and have relaxed
convergence requirements. However, they are typically used in offline
PI, whereas a central goal of reinforcement learning is to develop
*online* algorithms. Therefore, we propose an online PI
algorithm that evaluates policies with the so-called least-squares
temporal difference for Q-functions (LSTD-Q). The crucial difference
between this *online least-squares policy iteration* (LSPI)
algorithm and its offline counterpart is that, in the online case,
policy improvements must be performed once every few state
transitions, using only an incomplete evaluation of the current
policy. In an extensive experimental evaluation, online LSPI is found
to work well for a wide range of its parameters, and to learn
successfully in a real-time example. Online LSPI also compares
favorably with offline LSPI and with a different flavor of online PI,
which instead of LSTD-Q employs another least-squares method for
policy evaluation.

Corresponding technical report: pdf file (296 KB)

@inproceedings{BusErn:10-009,

author={L. Bu{\c{s}}oniu and D. Ernst and B. {D}e Schutter and R. Babu{\v{s}}ka},

title={Online least-squares policy iteration for reinforcement learning control},

booktitle={Proceedings of the 2010 American Control Conference},

address={Baltimore, Maryland},

pages={486--491},

month=jun # {--} # jul,

year={2010}

}

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