Dynamic deep-reinforcement-learning algorithm in Partially Observed
  Markov Decision Processes
Saki Omi Hyo-Sang Shin Namhoon Cho Antonios Tsourdos
Reinforcement learning has been greatly improved in recent studies and an
increased interest in real-world implementation has emerged in recent years. In
many cases, due to the non-static disturbances, it becomes challenging for the
agent to keep the performance. The disturbance results in the environment
called Partially Observable Markov Decision Process. In common practice,
Partially Observable Markov Decision Process is handled by introducing an
additional estimator, or Recurrent Neural Network is utilized in the context of
reinforcement learning. Both of the cases require to process sequential
information on the trajectory. However, there are only a few studies
investigating the effect of information to consider and the network structure
to handle them. This study shows the benefit of action sequence inclusion in
order to solve Partially Observable Markov Decision Process. Several structures
and approaches are proposed to extend one of the latest deep reinforcement
learning algorithms with LSTM networks. The developed algorithms showed
enhanced robustness of controller performance against different types of
external disturbances that are added to observation.