Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Sliding-Window Algorithm for Markov Decision Processes with
  Arbitrarily Changing Rewards and Transitions   We consider reinforcement learning in changing Markov Decision Processes
where both the state-transition probabilities and the reward functions may vary
over time. For this problem setting, we propose an algorithm using a sliding
window approach and provide performance guarantees for the regret evaluated
against the optimal non-stationary policy. We also characterize the optimal
window size suitable for our algorithm. These results are complemented by a
sample complexity bound on the number of sub-optimal steps taken by the
algorithm. Finally, we present some experimental results to support our
theoretical analysis.

12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom