Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Reinforcement Learning Using Recursive Least-Squares Methods   The recursive least-squares (RLS) algorithm is one of the most well-known
algorithms used in adaptive filtering, system identification and adaptive
control. Its popularity is mainly due to its fast convergence speed, which is
considered to be optimal in practice. In this paper, RLS methods are used to
solve reinforcement learning problems, where two new reinforcement learning
algorithms using linear value function approximators are proposed and analyzed.
The two algorithms are called RLS-TD(lambda) and Fast-AHC (Fast Adaptive
Heuristic Critic), respectively. RLS-TD(lambda) can be viewed as the extension
of RLS-TD(0) from lambda=0 to general lambda within interval [0,1], so it is a
multi-step temporal-difference (TD) learning algorithm using RLS methods. The
convergence with probability one and the limit of convergence of RLS-TD(lambda)
are proved for ergodic Markov chains. Compared to the existing LS-TD(lambda)
algorithm, RLS-TD(lambda) has advantages in computation and is more suitable
for online learning. The effectiveness of RLS-TD(lambda) is analyzed and
verified by learning prediction experiments of Markov chains with a wide range
of parameter settings. The Fast-AHC algorithm is derived by applying the
proposed RLS-TD(lambda) algorithm in the critic network of the adaptive
heuristic critic method. Unlike conventional AHC algorithm, Fast-AHC makes use
of RLS methods to improve the learning-prediction efficiency in the critic.
Learning control experiments of the cart-pole balancing and the acrobot
swing-up problems are conducted to compare the data efficiency of Fast-AHC with
conventional AHC. From the experimental results, it is shown that the data
efficiency of learning control can also be improved by using RLS methods in the
learning-prediction process of the critic. The performance of Fast-AHC is also
compared with that of the AHC method using LS-TD(lambda). Furthermore, it is
demonstrated in the experiments that different initial values of the variance
matrix in RLS-TD(lambda) are required to get better performance not only in
learning prediction but also in learning control. The experimental results are
analyzed based on the existing theoretical work on the transient phase of
forgetting factor RLS methods.

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The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: physics