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title
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
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OOTD Detection through Causal Active Experimentation.
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GalilAI
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2026-02-26 22:35:09
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GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL | GalilAI GalilAI OOTD Detection through Causal Active Experimentation. View My GitHub Profile GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL Abstract Out-of-distribution (OOD) detection is a well-studied topic in supervised learning. Extending the successes in supervised learning methods to the reinforcement learning (RL) setting, however, is difficult due to the data generating process - RL agents actively query their environment for data and this data is a function of the policy followed by the agent. Thus, an agent could neglect a shift in the environment if its policy did not lead it to explore the aspect of the environment that shifted. Therefore, to achieve safe and robust generalization in RL, there exists an unmet need for OOD detection through active experimentation. Here, we attempt to bridge this lacuna by fir...
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