Reannealing of Decaying Exploration Based On Heuristic Measure in Deep
  Q-Network
  Existing exploration strategies in reinforcement learning (RL) often either
ignore the history or feedback of search, or are complicated to implement.
There is also a very limited literature showing their effectiveness over
diverse domains. We propose an algorithm based on the idea of reannealing, that
aims at encouraging exploration only when it is needed, for example, when the
algorithm detects that the agent is stuck in a local optimum. The approach is
simple to implement. We perform an illustrative case study showing that it has
potential to both accelerate training and obtain a better policy.
