Computing an approximately optimal policy with high probability in this case is known as PAC RL with a generative model.

However, these traditional games are limited because they are typically designed based on heuristics.

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Underthisassumption,weintroduce IMED-RLandprove that its regret upper bound asymptotically matches the regret lower bound.

This paper focuses on MDPs with linearly realizable optimal Q-functionQ?.

Importantly, E2 is bounded independently ofS.

This property can be observed directly from Eq. 2 when integration is replaced by summation. Closed-loop First, we construct the standard, closed-loopα-vectors, which represent the value function under closed loop dynamics [1,5]. Each point in the scatter plot represents a paired experiment with identical target dynamics.