  In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise
the collected rewards while interacting with their environment while using some
prior knowledge that is accessed beforehand. Many BRL algorithms have already
been proposed, but even though a few toy examples exist in the literature,
there are still no extensive or rigorous benchmarks to compare them. The paper
addresses this problem, and provides a new BRL comparison methodology along
with the corresponding open source library. In this methodology, a comparison
criterion that measures the performance of algorithms on large sets of Markov
Decision Processes (MDPs) drawn from some probability distributions is defined.
In order to enable the comparison of non-anytime algorithms, our methodology
also includes a detailed analysis of the computation time requirement of each
algorithm. Our library is released with all source code and documentation: it
includes three test problems, each of which has two different prior
distributions, and seven state-of-the-art RL algorithms. Finally, our library
is illustrated by comparing all the available algorithms and the results are
discussed.
