Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Performance Bounds for Primal-Dual Reinforcement Learning from
  Demonstrations   We consider large-scale Markov decision processes with an unknown cost
function and address the problem of learning a policy from a finite set of
expert demonstrations. We assume that the learner is not allowed to interact
with the expert and has no access to reinforcement signal of any kind. Existing
inverse reinforcement learning methods come with strong theoretical guarantees,
but are computationally expensive, while state-of-the-art policy optimization
algorithms achieve significant empirical success, but are hampered by limited
theoretical understanding. To bridge the gap between theory and practice, we
introduce a novel bilinear saddle-point framework using Lagrangian duality. The
proposed primal-dual viewpoint allows us to develop a model-free provably
efficient algorithm through the lens of stochastic convex optimization. The
method enjoys the advantages of simplicity of implementation, low memory
requirements, and computational and sample complexities independent of the
number of states. We further present an equivalent no-regret online-learning
interpretation.

21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin