Welcome back, tech enthusiasts! In today’s thrilling video, we will dive into the world of deep reinforcement learning using OpenAI Gym and TensorFlow. Get ready to witness the power of cutting-edge technology as we embark on this exhilarating adventure.
But before we plunge into the tutorial, I have something fascinating to share with you. This video is proudly sponsored by IP Royal, a game-changer in the realm of data science and machine learning. You won’t want to miss this, as it directly relates to our journey. So, let’s not waste any more time and jump right into it!
Contents
Reinforcement Learning with OpenAI Gym
To kick off our deep reinforcement learning journey, we need to install a few external Python packages. Open up your command line and enter the following command:
pip install gymAdditionally, we will be using TensorFlow and Keras rl2, so make sure to install these packages if you haven’t already. For compatibility reasons, we will specify the exact version of OpenAI Gym as 0.25.2.
Once all the necessary packages are installed, let’s import the gym and random modules. We will start by showcasing the environment we will be working with – the CartPole environment. This environment involves balancing a stick or pole by moving left and right.
Now it’s time to run a few episodes with random actions. We will create a random agent that randomly selects whether to move left or right. After each action, we will observe the state and calculate the reward. By doing this, we can evaluate the performance of the random agent.
Training a Deep Neural Network
To improve upon the random agent’s performance, we will train a deep neural network using the Deep Q Learning algorithm. Prepare to witness the magic of artificial intelligence at work.
First, import the necessary modules from TensorFlow and Keras. We will use the Sequential model, Dense layer, and the Adam optimizer. From Keras rl2, we will import the DQN agent, Boltzmann Q policy, and Sequential memory.
Now, let’s build the neural network model. We will use a Sequential model and add a flattened layer with the input shape corresponding to the number of states. Next, add two dense layers with 24 neurons each, followed by a final dense layer with 2 neurons representing the possible actions.
Once the model is ready, we will convert it into a Deep Q Learning agent using the DQN agent. Set the necessary parameters such as memory capacity, exploration policy, and the number of actions. Finally, compile the model using the Adam optimizer with a specific learning rate.
Training and Testing the Deep Q Learning Agent
Now comes the exciting part – training the deep Q learning agent on the environment. Use the agent’s fit method to start the training process. We will train the agent for a specified number of steps, in this case, 100,000 steps. Although the training process isn’t visualized, you can modify the code to visualize it if you prefer.
After training, evaluate the agent’s performance by testing it on the environment. The agent’s test method allows us to observe its performance over multiple episodes. Finally, calculate the mean reward per episode to determine the agent’s success.
Conclusion
And there you have it, my friends! You’ve witnessed the marvels of deep reinforcement learning with OpenAI Gym and TensorFlow. We started with a random agent and witnessed its imperfect performance. However, with the power of deep Q learning, we trained an agent that mastered the task of balancing the CartPole perfectly.
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