AlgTUDelft/AlwaysSafe
Code for the paper "AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training"
This code helps researchers and practitioners in reinforcement learning develop and test AI agents that learn to perform tasks without ever violating critical safety rules during their training. It takes in a defined environment and safety constraints, and outputs a trained agent that adheres to these constraints throughout its learning process. This is for AI researchers and developers focused on building safe and reliable autonomous systems.
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Use this if you are developing reinforcement learning agents for safety-critical applications and need to ensure that no safety violations occur during the agent's training phase.
Not ideal if you are looking for a general-purpose reinforcement learning framework where safety during training is not a primary concern, or if you require an off-the-shelf solution without custom environment integration.
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Language
Python
License
MIT
Category
Last pushed
May 09, 2022
Commits (30d)
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