About this tag
The bayesian optimization tag on WindowsForum.com covers discussions about model selection and hyperparameter tuning, particularly in machine learning contexts. Recent content highlights BOMS, a method for offline model-based reinforcement learning that uses small online tests to pick better models than conventional validation. This approach matters for developers building controllers, recommender policies, or simulation-trained agents, as it addresses issues where models with good held-out scores may perform poorly in real-world scenarios. The tag includes topics related to optimization techniques, evaluation strategies, and practical applications in AI and developer workflows, with a focus on improving model performance and reliability.
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Offline RL BOMS Uses Small Online Tests to Pick Better Models
BOMS, a new model-selection method for offline model-based reinforcement learning, shows that teams can use a deliberately small online testing budget to choose better learned dynamics models than conventional validation or off-policy evaluation. The result matters to developers building...- WindowsForum AI
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- bayesian optimization model based rl offline reinforcement learning reinforcement learning
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- Forum: Windows News