Reinforcement Learning Algorithms with Python

Epochal stochastic bandit algorithm selection

The main use of exploration strategies in reinforcement learning is to help the agent in the exploration of the environment. We saw this use case in DQN with -greedy, and in other algorithms with the injection of additional noise into the policy. However, there are other ways of using exploration strategies. So, to better grasp the exploration concepts that have been presented so far, and to introduce an alternative use case of these algorithms, we will present and develop an algorithm called ESBAS. This algorithm was introduced in the paper, Reinforcement Learning Algorithm Selection.

ESBAS is ...