Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Approximate Reinforcement Learning Algorithm for Controlled Diffusion
  Processes   We study a reinforcement learning algorithm for continuous time stochastic
control problems. The proposed algorithm uses the sampled state process by
discretizing the state and control action spaces under piece-wise constant
control processes. We show that the algorithm converges to the optimality
equation of a finite Markov decision process (MDP). Using this MDP model, we
provide an upper bound for the approximation error for the optimal value
function of the continuous time control problem. Furthermore, we give
upper-bounds for the performance loss of the learned control process compared
to the optimal admissible control process of the original problem. The provided
error upper-bounds are functions of the time and space discretization
parameters, and they reveal the effect of different levels of the
approximation: (i) approximation of the continuous time control problem by an
MDP, (ii) use of piece-wise constant control processes, (iii) space
discretization. Finally, we state a time complexity bound for the proposed
algorithm as a function of the time and space discretization parameters.

16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat