Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Sample Complexity of Reinforcement Learning with a Generative
  Model   We consider the problem of learning the optimal action-value function in the
discounted-reward Markov decision processes (MDPs). We prove a new PAC bound on
the sample-complexity of model-based value iteration algorithm in the presence
of the generative model, which indicates that for an MDP with N state-action
pairs and the discount factor \gamma\in[0,1) only
O(N\log(N/\delta)/((1-\gamma)^3\epsilon^2)) samples are required to find an
\epsilon-optimal estimation of the action-value function with the probability
1-\delta. We also prove a matching lower bound of \Theta
(N\log(N/\delta)/((1-\gamma)^3\epsilon^2)) on the sample complexity of
estimating the optimal action-value function by every RL algorithm. To the best
of our knowledge, this is the first matching result on the sample complexity of
estimating the optimal (action-) value function in which the upper bound
matches the lower bound of RL in terms of N, \epsilon, \delta and 1/(1-\gamma).
Also, both our lower bound and our upper bound significantly improve on the
state-of-the-art in terms of 1/(1-\gamma).

17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th