  Effective and intelligent exploration has been an unresolved problem for
reinforcement learning. Most contemporary reinforcement learning relies on
simple heuristic strategies such as $\epsilon$-greedy exploration or adding
Gaussian noise to actions. These heuristics, however, are unable to
intelligently distinguish the well explored and the unexplored regions of state
space, which can lead to inefficient use of training time. We introduce
entropy-based exploration (EBE) that enables an agent to explore efficiently
the unexplored regions of state space. EBE quantifies the agent's learning in a
state using merely state-dependent action values and adaptively explores the
state space, i.e. more exploration for the unexplored region of the state
space. We perform experiments on a diverse set of environments and demonstrate
that EBE enables efficient exploration that ultimately results in faster
learning without having to tune any hyperparameter.
  The code to reproduce the experiments is given at
\url{https://github.com/Usama1002/EBE-Exploration} and the supplementary video
is given at \url{https://youtu.be/nJggIjjzKic}.
