Welcome to your friendly guide that will navigate you through the installation and usage of the ROS2Learn framework! This platform leverages various AI and reinforcement learning (RL) algorithms specifically crafted for robotic training within selected environments. Buckle up as we delve into the steps to unleash the potential of ROS2Learn!

What’s in the Repository?

This repository is a treasure trove for anyone interested in AI and robotics, featuring:

• Algorithms: Techniques for training and teaching robots.
• Environments: Pre-built settings to train selected robots.
• Experiments: Examples and utilities showcasing the repository’s capabilities.

For further reading, a whitepaper discussing this initiative can be found at arXiv: 1903.06282.

Installation

Prior to usage, installation is necessary. Here’s how:

• For source installation, refer to Install.md.
• If you prefer to use Docker, consult dockerREADME.md for installation and usage instructions.

Getting Started with Usage

Tuning Hyperparameters

First things first, ensure that you check the optimal network hyperparameters for the environment you intend to train. You can find this valuable information in Hyperparams.md.

Training Your Robot Agent

Training the robot is straightforward. For instance, if you want to train the MARA robot using the PPO2_MLP algorithm, execute the following command:

sh
cd ~ros2learn/experiments/examples/MARA
python3 train_ppo2_mlp.py

Here’s a helpful analogy: consider your robot as a young apprentice learning to bake. The ingredients (hyperparameters) must be adjusted for the cake (robot performance) to rise perfectly. If you want the apprentice to master different recipes (train with various environments), simply change the ‘recipe book’ in the algorithm.

Running a Trained Policy

Post-training, running a saved policy works similarly:

• Edit the trained_path value in the baselinesppo2defaults.py file to point to your trained model.
• Then, execute the script:

sh
cd ~ros2learn/experiments/examples/MARA
python3 run_ppo2_mlp.py -g -r -v 0.3

This command will let your newly-trained robot showcase its skills with proper visualization and real-time physics. Imagine it as sending the apprentice out to bake in front of an audience.

Visualizing Training Data on Tensorboard

To visualize the training process, use Tensorboard as follows:

sh
tensorboard --logdir=tmpros2learn/MARA/Collision-v0/ppo2_mlp --port 8008

The logs offer insights into the robot’s learning journey—think of this as reviewing the apprentice’s baking attempts to perfect the cake!

Custom Experimentations

Hyperparameter Tuning

1. Set your desired targets in the gym-gazebo2 environments.
2. Adjust hyperparameters in the baselines defaults script.

Creating Your Own Training Script

Follow these structured steps:

1. Create a session.
2. Retrieve hyperparameters from the algorithm’s defaults script.
3. Construct your environment.
4. Call the learning function of your chosen algorithm.

Optional: Enhance your script with Tensorboard statistics.

Troubleshooting

If you encounter any issues during setup or execution, do the following:

• Double-check your installation steps and consult the corresponding .md files.
• Ensure your code matches the example provided, especially paths and variable names.
• Verify the compatibility of algorithms with the chosen environments.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.