This project investigates how to drive exploration in reinforcement learning problems using the framework of best policy identification. The goal of the project is to demonstrate that the best policy identification methodology can be used to improve the exploration process in reinforcement learning problems. Results will be obtained by running experiments on several Gym and Mujoco environments.