Python Deep Learning Projects

The conclusion to the project

This project was to build a deep reinforcement learning model to successfully play the game of CartPole-v1 from OpenAI Gym. The use case of this chapter is to build a reinforcement learning model on a simple game environment and then extend it to other complex games such as Atari.

In the first half of this chapter, we built a deep Q-learning model to play the CartPole game. The DQN model during testing scored an average of 277.88 points over 100 games. 

In the second half of this chapter, we built a deep SARSA learning model (using the same epsilon-greedy policy as Q-learning) to play the CartPole game. The SARSA model during testing scored an average of 365.67 points over 100 games.

Now, let's follow the same ...