DEEP Q-LEARNING NETWORK (DQN) ALGORITHM

Reinforcement Learning is an intriguing subject of Machine Learning that is gaining prominence and attention. One key reason for its popularity is advances in Reinforcement Learning, which have enabled computer algorithms such as Alpha Go and OpenAI Five to attain human-level performance in games such as Go and Dota 2. The Deep Q-Learning algorithm is a key idea in Reinforcement Learning. Naturally, many of us are interested in learning more about the algorithms that enable these remarkable achievements. We'll share a basic Deep Q-Network implementation (minDQN) in this tutorial as a practical guide to assist new learners code their own Deep Q-Networks.

The figure above shows the CartPole environment, that is a straightforward environment in which the goal is to maneuver a cart left or right in order to keep an upright pole balanced for as long as feasible. The state space is represented by four values: Cart Position, Cart Velocity, Pole Angle, and Pole Velocity at the Tip. The action space is defined by two values (0 and 1), which allow the automobile to travel left or right at each time step.

THE MAIN CODE OF DEEP Q-LEARNING NETWORK CLUSTERING ALGORITHM

Full Code Of Implementing DQN Algorithm

We will see the example of how Cartpole environment could be apply, see the code below: