Master Reinforcement Learning with Python: Solve Gymnasium CartPole-v1 using Q-Learning

Master Reinforcement Learning with Python: Solve Gymnasium CartPole-v1 using Q-Learning

Table of Contents

1. Introduction
2. Problem Statement
3. Understanding the Q-Table
4. Dividing the Attributes into Segments
5. Initializing the Q-Table
6. Modifying the Parameters
7. Adding the New Attributes
8. Training the Robot
9. Testing the Trained Model
10. Conclusion

Introduction

In this tutorial, we will teach a robot how to balance a pole on its head using the concept of a Q-table. This problem, called "Cod pull," involves balancing a pole on top of a cart. We will represent the robot's position, velocity, angle, and angular velocity as state attributes. Our goal is to construct a Q-table that guides the robot's actions Based on its state. The Q-table will contain Q-values that determine whether the robot should move left or right to balance the pole.

Problem Statement

The problem we need to solve is to balance a pole on a cart using a Q-table. The robot can move along the x-axis, either to the left or right, at a certain velocity. We also have access to the angle and angular velocity of the pole. If the robot goes out of a certain range or if the pole falls to a certain angle, the game is over. Our task is to construct a Q-table that helps the robot make appropriate movements to balance the pole and prevent it from falling off the cart.

Understanding the Q-Table

To construct the Q-table, we need to represent the states of the robot using a combination of its position, velocity, angle, and angular velocity. However, since these attributes can have an infinite number of combinations, we need to divide them into segments. We divide the x-axis into segments to represent the position of the robot and similarly divide the angle, velocity range, and angular velocity range. By dividing these attributes into segments, we can represent the states in the Q-table.

Dividing the Attributes into Segments

By dividing the attributes into segments, we can handle the infinite combinations of the states. For example, we divide the x-axis into segments to represent the position of the robot. If the robot goes out of range, we consider it as position zero. For each segment within the range, we assign a specific position value. We do the same for the angle, velocity range, and angular velocity range. By dividing these attributes into segments, we can represent the states in the Q-table effectively.

Initializing the Q-Table

The Q-table is initialized as a multi-dimensional array that corresponds to the segments of the attributes. Since we divided the attributes into segments, the Q-table will have Dimensions based on the number of segments for each attribute. For example, if we have 10 segments for each attribute, the Q-table will be a 11x11x11x11x2 array. Each element in the Q-table represents a specific state-action pair and will hold the Q-value associated with that pair.

Modifying the Parameters

To train the robot effectively, we need to modify some parameters. We decrease the learning rate and increase the discount factor, which affects how much importance we give to future rewards. Additionally, we set the decay rate to a small value to slowly reduce exploration during training. These parameters may require some trial and error to find the best combination for solving the problem.

Adding the New Attributes

In the original Mountain car problem, we only had the position and velocity attributes. In the Cart Pole problem, we need to replace these attributes with position, velocity, angle, and angular velocity. We update the code to include these new attributes, and the calculation of the state is modified accordingly. By incorporating these attributes, we can Create a more accurate representation of the robot's state.

Training the Robot

To train the robot, we allow it to choose actions randomly initially and gradually transition to choosing the best action based on the Q-values. We update the code to include the angle and angular velocity attributes when selecting the action. After each action, we receive the new state of the system and update the Q-values based on the reward obtained. We Continue this process for a certain number of episodes until the robot learns how to balance the pole.

Testing the Trained Model

Once the robot has been trained, we can test its performance by turning off the training mode. We can render the environment and observe if the robot can balance the pole effectively. We print out the episode number and reward every 100 steps to monitor its progress.

Conclusion

In this tutorial, we successfully trained a robot to balance a pole on a cart using a Q-table. By dividing the attributes into segments and updating the Q-values based on rewards, the robot learned to make informed decisions to prevent the pole from falling off. By understanding the concept of a Q-table and implementing it in this problem, we gained insights into reinforcement learning and its practical applications.