Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Probabilistic Performance Bounds for Inverse Reinforcement
  Learning   In the field of reinforcement learning there has been recent progress towards
safety and high-confidence bounds on policy performance. However, to our
knowledge, no practical methods exist for determining high-confidence policy
performance bounds in the inverse reinforcement learning setting---where the
true reward function is unknown and only samples of expert behavior are given.
We propose a sampling method based on Bayesian inverse reinforcement learning
that uses demonstrations to determine practical high-confidence upper bounds on
the $\alpha$-worst-case difference in expected return between any evaluation
policy and the optimal policy under the expert's unknown reward function. We
evaluate our proposed bound on both a standard grid navigation task and a
simulated driving task and achieve tighter and more accurate bounds than a
feature count-based baseline. We also give examples of how our proposed bound
can be utilized to perform risk-aware policy selection and risk-aware policy
improvement. Because our proposed bound requires several orders of magnitude
fewer demonstrations than existing high-confidence bounds, it is the first
practical method that allows agents that learn from demonstration to express
confidence in the quality of their learned policy.

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The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph