Reinforcement Learning with Non-Markovian Rewards
  The standard RL world model is that of a Markov Decision Process (MDP). A
basic premise of MDPs is that the rewards depend on the last state and action
only. Yet, many real-world rewards are non-Markovian. For example, a reward for
bringing coffee only if requested earlier and not yet served, is non-Markovian
if the state only records current requests and deliveries. Past work considered
the problem of modeling and solving MDPs with non-Markovian rewards (NMR), but
we know of no principled approaches for RL with NMR. Here, we address the
problem of policy learning from experience with such rewards. We describe and
evaluate empirically four combinations of the classical RL algorithm Q-learning
and R-max with automata learning algorithms to obtain new RL algorithms for
domains with NMR. We also prove that some of these variants converge to an
optimal policy in the limit.
