Safety-Gymnasium: An Introduction
Safety-Gymnasium is a sophisticated and versatile library focused on Safe Reinforcement Learning (SafeRL). It stands out by offering an expansive range of environments specifically designed to benchmark SafeRL algorithms. The library incorporates a set of standard application programming interfaces (APIs) that are adeptly structured to handle information related to constraints, thus facilitating innovative explorations within the field.
Why Choose Safety-Gymnasium?
Safety-Gymnasium represents a significant advancement over existing SafeRL environments. Unlike other platforms, it is built on the robust foundation of MuJoCo 2.3.0+, providing comprehensive support for vectorized environments, the new Gym API, and vision input capabilities. Comparative analysis reveals that many older libraries haven't integrated these advancements, thus highlighting Safety-Gymnasium's relevance and up-to-date features that meet modern SafeRL needs.
Diverse Environments
Safety-Gymnasium offers a rich selection of environments to advance SafeRL research and application. These environments are crafted to address various learning tasks by integrating elements from the broader reinforcement learning community. Environments include:
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Safe Navigation: Tasks like Button, Goal, Push, and Circle, featuring diverse agents such as Point, Car, and Ant.
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Safe Velocity: Focused on managing the velocity of agents like HalfCheetah and Humanoid.
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Safe Vision: Incorporates tasks that demand enhanced visual processing, such as BuildingButton and Race.
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Safe Multi-Agent: Deals with dynamic interactions between multiple agents, like Multi-Goal and Multi-Agent Velocity tasks.
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Safe Isaac Gym: A set of tasks centered around more sophisticated interactions, using advanced simulation setups like FreightFranka and ShadowHands.
Supported Agents and Tasks
Safety-Gymnasium supports a wide array of agents designed to tackle these tasks efficiently. For instance, agents like Point, Car, and Doggo are utilized in Safe Navigation, while agents such as ShadowHands and FreightFranka cater to more complex environments in Safe Isaac Gym tasks. These agents are depicted in detailed simulations, providing users with a vivid understanding of the scenarios possible within Safety-Gymnasium's environments.
Technical Considerations
It's important to note that while deploying Safety-Gymnasium, Python 3.11 is not currently supported due to compatibility issues with pygame. Installation can be pursued through GitHub as the package is undergoing deployment adjustments before availability on PyPI.
Overall, Safety-Gymnasium is a comprehensive platform offering a robust and flexible framework for researchers and developers interested in expanding the frontiers of Safe Reinforcement Learning. By supporting diverse environments and cutting-edge features, it empowers users to innovate and experiment with high safety standards in control and navigation tasks.