Concurrent RLSVI with aggregated states is shown to have worst-case regret O~(K H^(5/2) Γ √N) and per-agent regret 1/√N, with an analogous infinite-horizon bound.
Improved worst-case regret bounds for randomized least-squares value iteration
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Concurrent Learning with Aggregated States via Randomized Least Squares Value Iteration
Concurrent RLSVI with aggregated states is shown to have worst-case regret O~(K H^(5/2) Γ √N) and per-agent regret 1/√N, with an analogous infinite-horizon bound.