| Definition | The Continuous Safety Gym is a suite of environments for safe reinforcement learning. |
| No of environments | 53 |
The Continuous Safety Gym is a suite of gym environments designed for safe RL applications derived from several established repositories. These environments integrate safety constraints into the RL problem, offering a platform to develop and assess agents’ performance while ensuring they operate within safe limits. Key tasks include navigating safely while performing actions like reaching goals, gathering objects, and avoiding obstacles, all while respecting safety boundaries to minimize risks.
The environments in Continuous Safety Gym are adapted from multiple sources, including Safe-Explorer, Bullet-Safety-Gym, and Multiagent-Particle-Env. Safe-Explorer focuses on implementing a “safety layer” that ensures safety in continuous action spaces by making minimal perturbations to the agent’s actions. This allows the agent to explore and learn while respecting predefined safety constraints. Bullet-Safety-Gym extends this framework by providing a variety of agents and tasks designed to test safety, such as moving through a circle while avoiding boundaries, navigating obstacles, or gathering items while avoiding hazards. These environments provide a diverse set of scenarios for agents to encounter, all governed by continuous cost functions and requiring the agent to learn to balance safety and reward.
The Continuous Safety Gym suite includes a broad range of environments, each presenting unique challenges for agents. Tasks like “Circle” require agents to move around a circular path while avoiding a safety zone breach, while “Gather” challenges agents to collect items while avoiding dangerous obstacles. “Reach” and “Run” tasks require agents to move towards goals or navigate through designated areas, all while incurring costs for violating safety constraints. The environments are designed to be flexible, offering agents the ability to explore safety constraints in single-agent or multi-agent settings. This suite is an important resource for research in constrained RL, helping to address the growing need for AI systems that can operate safely in dynamic, complex environments.