How to Start with End-to-End Robotic Reinforcement Learning

Welcome to the fascinating world of robotic reinforcement learning! This guide will help you understand how to implement the techniques described in the paper titled End-to-End Robotic Reinforcement Learning without Reward Engineering by Avi Singh and colleagues. We’ll take you through the installation process and provide handy troubleshooting tips.

What is Robotic Reinforcement Learning?

This field combines deep learning and reinforcement learning to train robots to perform tasks based solely on sensor input (e.g., images) rather than predefined rules. Imagine teaching a child how to stack blocks by using visual examples rather than manually explaining every step. This is the essence of the method presented in the paper: learning through visual examples, which require no extra reward engineering.

Prerequisites

Before diving into the installation process, ensure you meet the following requirements:

• You should have either Conda or Docker installed on your machine.
• Most environments require a MuJoCo license.

Getting Started with Installation

Conda Installation

Follow these steps to set up the environment using Conda:

1. Download MuJoCo: Visit the MuJoCo website and install version 1.50. Ensure the files are extracted to the default location (~/.mujoco/mjpro150).
2. License Setup: Copy your MuJoCo license key (mjkey.txt) to ~/.mujoco/mjkey.txt.
3. Clone the Repository: Open your terminal and clone the repository:

   git clone https://github.com/avisingh599/reward-learning-rl.git $REWARD_LEARNING_PATH

4. Create and Activate Environment: Run the following commands:

   cd $REWARD_LEARNING_PATH
   conda env create -f environment.yml
   conda activate softlearning
   pip install -e $REWARD_LEARNING_PATH

5. Deactivate Environment: Once done, you can deactivate and remove the environment using:

   conda deactivate
   conda remove --name softlearning --all

Docker Installation

If you prefer using Docker, follow these steps:

1. Setup: Make sure your MuJoCo key is stored correctly. Then, you can build and run the container using:

   export MJKEY=$(cat ~/.mujoco/mjkey.txt)
   docker-compose -f .docker/docker-compose.dev.gpu.yml up -d --force-recreate

2. Access the Container: Use the following command to get into the container:

   docker exec -it softlearning bash

3. Clean Up: To remove the Docker setup, run:

   docker-compose -f .docker/docker-compose.dev.gpu.yml down --rmi all --volumes

Training an Agent

Here’s how you can start training an agent using the softlearning library:

softlearning run_example_local examples.classifier_rl --n_goal_examples 10 --task=Image48SawyerDoorPullHookEnv-v0 --algorithm VICERAQ --num-samples 5 --n_epochs 300 --active_query_frequency 10

This command trains an agent to perform a specific task from the paper. You can adjust various parameters like the algorithm used or the number of goal examples to experiment further.

Examples and Further Exploration

The implementations offered in this repo include several tasks such as visual pushing and door opening, among others. Delve into the code provided and test out different scenarios.

Troubleshooting

If you run into any issues, consider the following:

• Ensure your MuJoCo installation is correct and that your license is in place.
• Check if docker and docker-compose are installed correctly and that your environment variables are set appropriately.
• If you encounter permission issues while executing Docker commands, try running them as sudo.
• If the code does not execute as expected, look for compatibility issues or consider consulting the project’s GitHub issues page.

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

Conclusion

This guide should provide you with a solid foundation for exploring robotic reinforcement learning without reward engineering. A remarkable journey awaits! 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.