Master Gymnasium MoJoCo Humanoid-v4 with Python & Stable Baseline3

Table of Contents

1. Introduction
2. Gymnasium Library
   • Installation
3. Stable Baseline 3 Library
   • Installation
4. Code Overview
   • Importing Libraries
   • Training and Testing Functions
   • Creating the Model
5. Training the Humanoid
   • Selecting the Algorithm
   • Setting the Neural Network Type
   • Specifying Graphics Card Usage
   • Training Steps and Logging
6. Understanding the Algorithms
   • Soft Actor-Critic
   • Twin Delayed Deep Deterministic Policy Gradient
   • AdVantage Actor-Critic
7. Customizing the Model
   • Learning Rate and Discount Factors
8. Running the Training
   • Command Line Instructions
   • Monitoring the Logs
9. Testing the Trained Model
   • Loading the Model
   • Predicting Actions and States
10. Results and Conclusion
    • Analyzing Training Progress
    • Evaluating the Trained Models
    • Final Thoughts

Introduction

In this tutorial, we will explore the implementation of reinforcement learning algorithms using the Stable Baseline 3 library. Specifically, we will train a humanoid to walk using Gymnasium environments. Reinforcement learning becomes more complex as the environments become more challenging, and in this tutorial, we will demonstrate how to Apply sophisticated algorithms to solve complex tasks.

Gymnasium Library

Before we dive into the implementation, let's ensure that we have the necessary libraries installed. The primary library we will be using is Gymnasium, which provides a collection of pre-built environments for reinforcement learning. To install Gymnasium, You can follow the steps below:

1. Visit the Gymnasium Website.
2. Copy the provided install command.
3. Execute the install command in your command prompt or terminal.

Stable Baseline 3 Library

Another essential library for this tutorial is Stable Baseline 3. This library provides various reinforcement learning algorithms that we can utilize to train our humanoid to walk. To install Stable Baseline 3, follow the steps below:

1. Open your command prompt or terminal.

2. Use the PIP Package manager to execute the following command:

   pip install stable-baseline3[extra]

   Make sure to include the [extra] part, as it will install Tensorboard, which we will discuss later.

Code Overview

Let's now take a closer look at the code we will be using for this tutorial. This will give us a better understanding of the implementation steps involved.

First, we import the necessary libraries, including Gymnasium and three reinforcement learning algorithms from Stable Baseline 3. We also import some additional Python libraries and Create directories for storing the training models and logs.

Next, we define two functions: one for training the model and another for testing the model. In the training function, we pass the Gymnasium environment and the selected algorithm to create our Stable Baseline model. There are several algorithms available in the library, so we have the flexibility to choose Based on our needs. We use a multi-layer perceptron (MLP) neural network as the default choice for our model.

Once we have declared the model, we start the training process by calling the model.learn function. This function trains the model indefinitely until We Are satisfied with the results. We specify the number of steps to train (in this case, 25,000) and save a version of the model for testing while training is ongoing.

During training, the model's progress and performance are logged. We can view these logs using Tensorboard, a visualization tool provided by Stable Baseline 3. Tensorboard allows us to monitor and analyze the training results in real-time.

This concludes the overview of the code structure. In the next sections, we will Delve into the specifics of training the humanoid and understanding the reinforcement learning algorithms used.

Pros

• Allows for the training of complex tasks using reinforcement learning algorithms.
• Provides pre-built Gymnasium environments for easy implementation.
• Allows customization of the neural network type and training parameters.
• Includes logging and visualization tools for monitoring training progress.

Cons

• Requires installation of Gymnasium and Stable Baseline 3 libraries.
• May require some knowledge of reinforcement learning concepts and algorithms.
• Training a humanoid to walk may require significant computational resources.

Training the Humanoid

To train the humanoid and make it capable of walking, we need to perform several steps using the Gymnasium and Stable Baseline 3 libraries. In this section, we will Outline these steps to provide a clear roadmap for the training process.

Selecting the Algorithm

Stable Baseline 3 offers a variety of reinforcement learning algorithms to choose from. In our case, we have selected three algorithms to train with: Soft Actor-Critic, Twin Delayed Deep Deterministic Policy Gradient (TD3), and Advantage Actor-Critic (AC2). These algorithms have shown promising results in similar tasks.

Setting the Neural Network Type

When creating our model, we have the option to choose between a multi-layer perceptron (MLP) neural network or a convolutional neural network (CNN). Since our task does not involve image recognition, we will stick with MLP as the default choice.

Specifying Graphics Card Usage

If you have an Nvidia graphics card, you can enable GPU acceleration by passing Cuda as a parameter during training. This allows for faster computation and training. If you do not have a graphics card, simply pass CPU to utilize your computer's CPU.

Training Steps and Logging

To begin training, we call the model.learn function and specify the number of steps we want the model to train for. In our case, we have chosen 25,000 steps as a starting point. After every step, the model's progress and training logs are saved for analysis.

During training, the episode length and rewards are tracked and logged. These metrics indicate the AI's ability to walk for longer periods and its accumulation of rewards over time. By analyzing these logs, we can determine the effectiveness of the training process and make adjustments if needed.

Understanding the Algorithms

Before diving into the training process, let's briefly discuss the reinforcement learning algorithms we will be using: Soft Actor-Critic, Twin Delayed Deep Deterministic Policy Gradient (TD3), and Advantage Actor-Critic (AC2).

• Soft Actor-Critic: This algorithm combines the actor-critic architecture with an entropy regularization term to encourage exploration and learn more robust policies.

• Twin Delayed Deep Deterministic Policy Gradient (TD3): TD3 improves upon the original Deep Deterministic Policy Gradient (DDPG) algorithm by utilizing twin critics and delayed policy updates. It is known for its stability and ability to handle continuous action spaces.

• Advantage Actor-Critic (AC2): AC2 is a synchronous variant of A3C (Asynchronous Advantage Actor-Critic). It uses multiple Parallel actors to Collect experiences and update the policy based on the computed advantages.

These algorithms differ in their approaches and strengths, and by training our humanoid with multiple algorithms, we can compare their performance and select the most effective one for our task.

Customizing the Model

Although Stable Baseline 3 provides default values for important parameters like learning rate and discount factors, we have the option to customize them based on our specific needs. These parameters influence the training process and can affect the model's performance. However, the default values usually work well, so changing them is not always necessary.

Running the Training

To start the training process, we need to execute specific commands in the command prompt or terminal. Let's go over the necessary steps to initiate the training:

1. Open a new command prompt or terminal.
2. Navigate to the directory containing the script file.

3. Execute the following command:

   python <script_name.py> <environment_name> <algorithm> -t

   • Replace <script_name.py> with the name of your Python script file.
   • Replace <environment_name> with the desired Gymnasium environment name (e.g., humanoid-v4).
   • Replace <algorithm> with the selected algorithm (e.g., sac for Soft Actor-Critic).
   • The -t flag indicates that we want to start the training process.

By following these steps, the model will start training, and you will see the progress and logging information in the command prompt or terminal.

Monitoring the Logs

To monitor and analyze the training progress, we can use Tensorboard. Tensorboard is a visualization tool provided by Stable Baseline 3 that allows us to track various metrics and view training logs in real-time. Here's how you can use Tensorboard:

1. Open a new command prompt or terminal.
2. Navigate to the directory containing the script file.

3. Execute the following command:

   tensorboard --logdir=<logs_directory>

   • Replace <logs_directory> with the path to the directory where the training logs are stored.

After executing the command, a local web server will start, and you can access Tensorboard by clicking the provided link. From Tensorboard, you can monitor and analyze metrics such as episode length and rewards over time.

Testing the Trained Model

Once training is complete, we can test the trained model to evaluate its performance. To do this, we need to execute specific commands similar to how we started the training process. Here's how you can test the trained model:

1. Open a new command prompt or terminal.
2. Navigate to the directory containing the script file.

3. Execute the following command:

   python <script_name.py> <environment_name> <algorithm> -s <path_to_model>

   • Replace <script_name.py> with the name of your Python script file.
   • Replace <environment_name> with the desired Gymnasium environment name (e.g., humanoid-v4).
   • Replace <algorithm> with the selected algorithm (e.g., sac for Soft Actor-Critic).
   • The -s flag indicates that we want to perform testing.
   • Replace <path_to_model> with the path to the saved model file.

By following these steps, the trained model will be loaded, and the humanoid's actions will be predicted based on the Current state. The resulting animation will demonstrate the model's performance in walking.

Results and Conclusion

After the training process and testing the models, we can evaluate the results and draw conclusions. In this tutorial, we trained three models using Soft Actor-Critic, Twin Delayed Deep Deterministic Policy Gradient (TD3), and Advantage Actor-Critic (AC2) algorithms.

By analyzing the training logs and monitoring the episode length and rewards over time on Tensorboard, we can determine the effectiveness of each algorithm. From our evaluation, it appears that the Soft Actor-Critic algorithm (Sac) shows the most promising progress and continuous improvement over time. The AC2 model seems to struggle, while the TD3 model does not make significant progress.

In conclusion, the combination of Gymnasium and Stable Baseline 3 libraries provides powerful tools for training complex tasks using reinforcement learning algorithms. By selecting the right algorithm, customizing the model, and monitoring the training progress, we can achieve impressive results, such as training a humanoid to walk.

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