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Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring ...
As of January 2025, API usage for the full o1 model is limited to developers on usage tier 5. [9] OpenAI noted that o1 is the first of a series of "reasoning" models. OpenAI shared in December 2024 benchmark results for its successor, o3 (the name o2 was skipped to avoid trademark conflict with the mobile carrier brand named O2). [10]
An online model is also sometimes referred to as an "online simulator" [1] [2] or "online system". [3] An online model is related to the concept of real-time simulation, as an online model runs in real-time by definition. Conversely, a real-time simulation is not necessarily an online model as it by definition does not require adaptivity in ...
For this purpose, gates instead of features act as players in a coalitional game with a value function that depends on measurements of the quantum circuit of interest. Additionally, a quantum version of the classical technique known as LIME (Linear Interpretable Model-Agnostic Explanations) [99] has also been proposed, known as Q-LIME. [100]
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In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
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