Learning in Markov Decision Processes under Constraints. (arXiv:2002.12435v5 [cs.LG] UPDATED)

We consider reinforcement learning (RL) in Markov Decision Processes in which
an agent repeatedly interacts with an environment that is modeled by a
controlled Markov process. At each time step $t$, it earns a reward, and also
incurs a cost-vector consisting of $M$ costs. We design model-based RL
algorithms that maximize the cumulative reward earned over a time horizon of
$T$ time-steps, while simultaneously ensuring that the average values of the
$M$ cost expenditures are bounded by agent-specified thresholds
$c^{ub}_i,i=1,2,\ldots,M$. …

arxiv decision learning markov processes