  This paper offers a new hybrid probably approximately correct (PAC)
reinforcement learning (RL) algorithm for Markov decision processes (MDPs) that
intelligently maintains favorable features of its parents. The designed
algorithm, referred to as the Dyna-Delayed Q-learning (DDQ) algorithm, combines
model-free and model-based learning approaches while outperforming both in most
cases. The paper includes a PAC analysis of the DDQ algorithm and a derivation
of its sample complexity. Numerical results are provided to support the claim
regarding the new algorithm's sample efficiency compared to its parents as well
as the best known model-free and model-based algorithms in application.
