Track trials, findings, and clinical evidence as it lands.
Search papers, labs, and topics across Lattice.
This paper investigates the efficacy of Mixture-of-Experts (MoE) architectures for cryptocurrency order execution using Double Deep Q-Learning (DDQL) on BTC/USDT limit order book data. Contrary to expectations, MoE configurations did not significantly improve mean implementation shortfall over vanilla DDQL, and all learned policies underperformed simple benchmarks like TWAP. Crucially, MoE architectures, particularly $K{\geq}4$, effectively suppressed a critical failure mode where policies learned to wait for forced liquidation, a problem largely mitigated by annealed exploration alone.
Mixture-of-Experts for order execution masks training failures rather than improving performance, with annealed exploration proving more critical for stability.
Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect favourable training realisations rather than reproducible properties of the architecture. We evaluate vanilla Double Deep Q-Learning (DDQL), K-means-partitioned mixtures of DDQL experts at $K \in \{2, 4, 8\}$, and dense networks parameter-matched to the $K{=}4$ and $K{=}8$ expert budgets on 5-minute mean-aggregated BTC/USDT limit order book data from Binance. No learned configuration significantly improves mean implementation shortfall over DDQL. Under the reported specification, all have higher mean shortfall than TWAP (0.39 bps) and immediate liquidation (0.21 bps) in an environment whose frictionless replay and terminal-urgency penalty make early liquidation nearly costless; 11/100 vanilla-DDQL runs, versus none in either MoE $K{\geq}4$ arm, converge to a policy that waits until forced liquidation. We then decompose this specification on a device-matched baseline. Annealed exploration alone eliminates observed collapses (12/100 to 0/100; exact McNemar $p{=}4.9{\times}10^{-4}$), matching the elimination under expert partitioning. Combining annealed exploration with the aligned reward restores collapse in 19/30 runs; with all three specification changes, it rises to 48/100. In this environment, expert partitioning is unnecessary to suppress collapse and appears to mask a training-specification failure rather than confer an intrinsic performance benefit. No MoE $K{=}8$ run collapses under any of the six specifications tested. Across-seed dispersion is lowest at $K{=}8$ but non-monotone and not robust to family-wise adjustment, while within-policy tail risk worsens monotonically with $K$. The apparent attribution of the failure mode reverses between 30 and 100 seeds, illustrating the importance of repeated-seed evaluation.