  In practical applications, we can rarely assume full observability of a
system's environment, despite such knowledge being important for determining a
reactive control system's precise interaction with its environment. Therefore,
we propose an approach for reinforcement learning (RL) in partially observable
environments. While assuming that the environment behaves like a partially
observable Markov decision process with known discrete actions, we assume no
knowledge about its structure or transition probabilities.
  Our approach combines Q-learning with IoAlergia, a method for learning Markov
decision processes (MDP). By learning MDP models of the environment from
episodes of the RL agent, we enable RL in partially observable domains without
explicit, additional memory to track previous interactions for dealing with
ambiguities stemming from partial observability. We instead provide RL with
additional observations in the form of abstract environment states by
simulating new experiences on learned environment models to track the explored
states. In our evaluation, we report on the validity of our approach and its
promising performance in comparison to six state-of-the-art deep RL techniques
with recurrent neural networks and fixed memory.
