In the previous blog post, I learnt to implement the Q-learning algorithm using the Q-table. However, Q-tables are only plausible if there is a low number of states and actions. In this post, we will be implementing Deep Q-Network (DQN). By using neural networks, we are effectively replacing the Q-table with a neural network.
Reinforcement Learning: Deep Q-Network (DQN) with Open AI Taxi
Python code in a Jupyter notebook to implement Deep Q-Network (DQN) using the Open AI Taxi environment and Keras-RL