All experiments are run on Intel Xeon ICX Platinum 8358 and GeForce RTX 3090.

Furthermore, the theoretical guarantees of the transitivity and aggregation error bound are justified.

Incontrast,our work is concerned with an overall limit on the total amount of information an agent may acquire fromtheenvironment and,inturn,howthattranslates intoitsselection ofafeasible learning target.

As efficient exploration in BAMDPs hinges upon the judicious acquisition of information, our complexity measure highlights the worst-case difficulty of gathering information and exhausting epistemic uncertainty.

While mathematically elegant, a fundamental issue with AIXI is that it is only asymptotically computable.