Safety-Gymnasium is a highly adaptable library specifically designed for Safe Reinforcement Learning (SafeRL). It provides an extensive framework to benchmark SafeRL algorithms alongside customizable environments. In this article, we will walk you through the installation process, the features offered by Safety-Gymnasium, and how you can troubleshoot common issues you may encounter.

Installation: Setting Up Safety-Gymnasium

To make use of Safety-Gymnasium, you first need to install it on your machine. Here’s a detailed approach:

• Install from PyPI:

  pip install safety-gymnasium

• Install from Source:

  
  conda create -n envname python=3.8
  conda activate envname
  git clone https://github.com/PKU-Alignment/safety-gymnasium.git
  cd safety-gymnasium
  pip install -e .
          

Understanding Safety-Gymnasium Code with an Analogy

Let’s say you are setting up a playground with various games to make it engaging for children, similar to creating an environment in Safety-Gymnasium. However, the games need specific rules (just like you define tasks in Safety-Gymnasium) to ensure safety and fun.

In the example below, think of the code as setting up games where you need to decide the rules (tasks), the obstacles (geometries), and how to keep the players safe (cost measures). Each rule is a line in the code, outlining what happens during the game.

# Define a task with rules and settings
from safety_gymnasium.bases import BaseTask

class MyTaskLevel0(BaseTask):
    def __init__(self, config):
        super().__init__(config=config)
        self.num_steps = 500
        self.agent.placements = [(-0.8, -0.8, 0.8, 0.8)]
        self.agent.keepout = 0
        self.lidar_conf.max_dist = 6

    def calculate_reward(self):
        reward = 1
        return reward

Exploring the Environments

Safety-Gymnasium includes various tasks classified under categories like Safe Navigation, Safe Vision, and Safe Multi-Agent environments. For example, the tasks range from navigating towards a goal, pushing buttons, to complex multi-agent simulations.

Troubleshooting Common Issues

While working with Safety-Gymnasium, you may run into some issues. Here are some common fixes:

• If you experience rendering issues:

  
  echo export MUJOCO_GL=osmesa  ~.bashrc
  source ~.bashrc
  apt-get install libosmesa6-dev
  apt-get install python3-opengl
          

• For Python version issues:
  • Make sure you are using Python 3.8, as versions above can cause compatibility issues.
• Debugging with Keyboard:
  • Append a Debug suffix to the task name (e.g., SafetyCarGoal2Debug-v0) for manual control using keys I, K, J, and L.

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Final Thoughts

Safety-Gymnasium serves as an invaluable resource for researchers and practitioners in SafeRL, encouraging innovative environments and safe learning methodologies. We hope this guide has provided a clear pathway to utilizing Safety-Gymnasium effectively.

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.