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Full Research Paperbacks 2024, May 610, 2024, Auckland, New ZealandSafe Reinforcement Learning with Free form Natural Language Constraints and PreTrained Language Models Hangzhou Lounge ZhangZiyan
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Safe reinforcement learning refers to a framework within machine learning that emphasizes training agents in a manner that avoids harmful consequences or risks, ensuring that the learned policies are safe to deploy in real-world environments.
Researchers, developers, and organizations that utilize reinforcement learning methods in their projects, particularly those that have safety implications, are typically required to file documentation regarding safe reinforcement learning practices.
Filling out documentation for safe reinforcement learning typically involves providing detailed descriptions of the learning process, safety measures implemented, performance metrics, and any risk assessments conducted during development.
The purpose of safe reinforcement learning is to ensure that autonomous agents can learn effective strategies while minimizing the potential for unintended harmful behavior and promoting safe operational practices.
Reports on safe reinforcement learning must include information on the algorithms used, safety protocols in place, risk analysis results, data sources, and any demonstrations of the effectiveness and safety of the learned policies.
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