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The concepts of on-policy vs off-policy and online vs offline are separate, but do interact to make certain combinations more feasible. When looking at this, it is worth also considering the difference between prediction and control in Reinforcement Learning (RL). Online vs Offline These concepts are not specific to RL, many learning systems can be ...


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I assume you intended to write compute the evaluation metric over the validation set in batches; you do not compute loss over the validation set! That is quite a standard practice in many academic implementations (because, when the validation set is large enough, the memory will be a constraint), however, be sure to take the average of the values over all ...


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We have deployed one project in the real world that uses offline RL algorithms. Evaluating the performance of a policy is indeed a very tricky problem. Unfortunately, most existing OPE method is not really matured enough for many practical problems, especially when evaluating relative complex tasks and policies. The final solution we use in the end is ...


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