Mastering the Cart Pole Game with Neural Networks

Mastering the Cart Pole Game with Neural Networks

Table of Contents:

1. Introduction
2. Creating the Cartpole Class
3. Setting Up the Environment
4. Taking Actions and Observations
5. Building the Memory
6. Creating the Neural Network
7. Training the Neural Network
8. Adding Randomness to Exploration
9. Continuous Training Until Success
10. Testing the Trained Model
11. Conclusion

Introduction

In this article, we will explore how to solve the OpenAI Gym's CartPole problem using reinforcement learning and a neural network. We will walk through the process step by step, building the necessary components and explaining the underlying concepts. By the end of this article, You will have a solid understanding of how to train a neural network to control the CartPole environment and keep the pole upright.

Creating the Cartpole Class

To begin, we need to Create a class called CartPole that will handle the initialization and running of our reinforcement learning algorithm. This class will set up the necessary variables, such as the environment and the number of attempts we want to make. We will also define a method called run that will loop through the specified number of attempts and perform the necessary actions.

Setting Up the Environment

In this section, we will set up the CartPole environment using OpenAI Gym. The environment consists of a cart and a pole, and our goal is to keep the pole balanced on top of the cart for as long as possible. We will initialize the environment, reset it to the starting state, and retrieve the initial state object, which contains four data points: the position, velocity, angle, and pole velocity.

Taking Actions and Observations

Next, we need to define how our agent will Interact with the environment by taking actions and receiving observations. We will create a method called get_action that takes the Current state as input and returns either 0 or 1, representing the action of moving left or right. We will also define a function called step_environment that takes an action as input and steps the environment accordingly, returning the new observation, reward, and whether the cart has fallen over.

Building the Memory

In order to train our neural network, we need to build a memory of past actions, observations, rewards, and terminal states. We will use a data structure called a deque to store this memory, allowing us to efficiently append new entries and remove old ones when the memory becomes full. Each entry in the memory will contain the current state, the action taken, the reward received, the new state, and a flag indicating whether the cart has fallen over.

Creating the Neural Network

Now it's time to create a neural network that will serve as our function approximator. We will use the TensorFlow library to build a sequential model with fully connected layers. The input layer will have four nodes, corresponding to our four data points, and the output layer will have two nodes, representing the possible actions (0 or 1). We will compile the model with a mean squared error loss function and an Adam optimizer.

Training the Neural Network

With the neural network architecture set up, we can now train it using the q-learning algorithm. We will retrieve a mini-batch of experiences from the memory and feed them into the network. For each experience, we will calculate the target q-value Based on the immediate reward and the predicted q-value of the next state. We will update the network's weights using backpropagation and repeat this process for a specified number of epochs.

Adding Randomness to Exploration

To improve the learning process, we need to introduce randomness into the agent's exploration strategy. We will define an exploration value (e-value) that starts at 1 and gradually decreases over time. If a randomly generated number is less than or equal to the e-value, the agent will take a random action instead of relying on the neural network's prediction. This ensures that the agent explores different actions and avoids getting stuck in a suboptimal policy.

Continuous Training Until Success

Training a neural network for the CartPole problem can be a challenging task. In this section, we will implement a continuous training loop that runs until the agent successfully achieves 500 consecutive steps without the pole falling over. If the agent fails to reach this goal within a specified number of attempts, it will start the training process again from scratch. This approach allows the agent to Continue learning and improving its performance over time.

Testing the Trained Model

After training the neural network, we can test its performance by running the environment with the learned policy. We will create a separate file to load the trained model and use it to control the cart in the CartPole environment. By observing the agent's behavior, we can evaluate the effectiveness of our trained model and assess its ability to keep the pole balanced for extended periods.

Conclusion

In conclusion, we have explored the process of solving the CartPole problem using reinforcement learning and a neural network. We have covered the necessary steps, from setting up the environment and creating the neural network to training the model and testing its performance. By following this guide, you should now have a solid foundation for implementing your own reinforcement learning algorithms in various environments. Happy coding!

Highlights:

• Learn how to solve the CartPole problem using reinforcement learning and a neural network
• Set up the CartPole environment using OpenAI Gym
• Take actions and receive observations from the environment
• Build a memory to store past experiences for training
• Create a neural network architecture and train it using q-learning
• Add randomness to the agent's exploration strategy for better learning
• Implement a continuous training loop until the agent achieves success
• Test and evaluate the performance of the trained model in the CartPole environment

FAQ

Q: What is the CartPole problem? A: The CartPole problem is a classic control problem in the field of reinforcement learning. It involves balancing a pole on top of a cart by applying appropriate actions to keep the system stable.

Q: Why is q-learning used in this solution? A: Q-learning is a popular reinforcement learning algorithm that allows an agent to learn an optimal policy for making decisions in a given environment. In the context of the CartPole problem, q-learning helps the agent learn which actions to take to keep the pole balanced.

Q: How does the neural network learn in this solution? A: The neural network is trained using a q-learning algorithm. It learns by minimizing the difference between the predicted q-values and the target q-values, which are calculated based on the immediate reward and the predicted q-values of the next state.

Q: How long does it take for the agent to reach 500 steps? A: The training time can vary depending on various factors, such as the complexity of the problem, the architecture of the neural network, and the randomness in the exploration strategy. It may take several attempts and training iterations before the agent reaches 500 steps consistently.

Q: Can this solution be applied to other control problems? A: Yes, the concepts and techniques used in this solution can be applied to other control problems with some modifications. The key idea is to use a reinforcement learning algorithm, such as q-learning, and train a neural network to approximate the optimal policy for the given environment.

Q: How can I further improve the performance of the agent? A: There are several ways to improve the performance of the agent. You can try adjusting the neural network architecture, exploring different exploration strategies, increasing the training iterations, or using more advanced reinforcement learning algorithms.

Q: Is this solution applicable to real-world scenarios? A: While the CartPole problem is a simplified and controlled environment, the concepts and techniques used in this solution can be applied to real-world scenarios with some modifications and adaptations. The principles of reinforcement learning and neural networks can be used to tackle more complex and practical control problems.