Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Sample Complexity of Actor-Critic Method for Reinforcement
  Learning with Function Approximation   Reinforcement learning, mathematically described by Markov Decision Problems,
may be approached either through dynamic programming or policy search.
Actor-critic algorithms combine the merits of both approaches by alternating
between steps to estimate the value function and policy gradient updates. Due
to the fact that the updates exhibit correlated noise and biased gradient
updates, only the asymptotic behavior of actor-critic is known by connecting
its behavior to dynamical systems. This work puts forth a new variant of
actor-critic that employs Monte Carlo rollouts during the policy search
updates, which results in controllable bias that depends on the number of
critic evaluations. As a result, we are able to provide for the first time the
convergence rate of actor-critic algorithms when the policy search step employs
policy gradient, agnostic to the choice of policy evaluation technique. In
particular, we establish conditions under which the sample complexity is
comparable to stochastic gradient method for non-convex problems or slower as a
result of the critic estimation error, which is the main complexity bottleneck.
These results hold in continuous state and action spaces with linear function
approximation for the value function. We then specialize these conceptual
results to the case where the critic is estimated by Temporal Difference,
Gradient Temporal Difference, and Accelerated Gradient Temporal Difference.
These learning rates are then corroborated on a navigation problem involving an
obstacle, providing insight into the interplay between optimization and
generalization in reinforcement learning.

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The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio