  Robotic navigation concerns the task in which a robot should be able to find
a safe and feasible path and traverse between two points in a complex
environment. We approach the problem of robotic navigation using reinforcement
learning and use deep $Q$-networks to train agents to solve the task of robotic
navigation. We compare the Entropy-Based Exploration (EBE) with the widely used
$\epsilon$-greedy exploration strategy by training agents using both of them in
simulation. The trained agents are then tested on different versions of the
environment to test the generalization ability of the learned policies. We also
implement the learned policies on a real robot in complex real environment
without any fine tuning and compare the effectiveness of the above-mentioned
exploration strategies in the real world setting. Video showing experiments on
TurtleBot3 platform is available at \url{https://youtu.be/NHT-EiN_4n8}.
